From 9c08dd422950323e6352cae427f2ec9b0e804bc2 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Wed, 14 Jul 2021 17:54:31 +0200 Subject: [PATCH 01/80] add a callback to the generator --- c3/generator/generator.py | 15 +++++++++++++-- examples/two_qubits.ipynb | 28 ++++++++++++++++++++++++++++ 2 files changed, 41 insertions(+), 2 deletions(-) diff --git a/c3/generator/generator.py b/c3/generator/generator.py index 840a35d7..9095015d 100755 --- a/c3/generator/generator.py +++ b/c3/generator/generator.py @@ -10,7 +10,7 @@ """ import copy -from typing import List +from typing import List, Callable import hjson import numpy as np import tensorflow as tf @@ -29,11 +29,17 @@ class Generator: Physical or abstract devices in the signal processing chain. resolution : np.float64 Resolution at which continuous functions are sampled. + callback : Callable + Function that is called after each device in the signal line. """ def __init__( - self, devices: dict = None, chains: dict = None, resolution: np.float64 = 0.0 + self, + devices: dict = None, + chains: dict = None, + resolution: np.float64 = 0.0, + callback: Callable = None, ): self.devices = {} if devices: @@ -44,6 +50,7 @@ def __init__( self.__check_signal_chains() self.resolution = resolution self.gen_stacked_signals: dict = None + self.callback = callback def __check_signal_chains(self) -> None: for channel, chain in self.chains.items(): @@ -128,6 +135,10 @@ def generate_signals(self, instr: Instruction) -> dict: outputs = dev.process(instr, chan, *inputs) signal_stack.append(outputs) gen_stacked_signals[chan].append((dev_id, copy.deepcopy(outputs))) + + # call the callback with the current signal + if self.callback: + self.callback(chan, dev_id, outputs) # The stack is reused here, thus we need to deepcopy. gen_signal[chan] = copy.deepcopy(signal_stack.pop()) self.gen_stacked_signals = gen_stacked_signals diff --git a/examples/two_qubits.ipynb b/examples/two_qubits.ipynb index 480bcdf1..17cb46ac 100644 --- a/examples/two_qubits.ipynb +++ b/examples/two_qubits.ipynb @@ -534,6 +534,34 @@ " )" ] }, + { + "cell_type": "markdown", + "source": [ + "Optionally, we can look at the signal generated by each device by setting a callback." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "generator.callback = lambda chain_id, device_id, signal: (\n", + " # do something\n", + ")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, { "cell_type": "markdown", "metadata": {}, From 9df3003d9a28cb77249a98665b67bce86f3629ad Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Mon, 19 Jul 2021 18:16:42 +0200 Subject: [PATCH 02/80] iterate devices as a directed graph in the generator --- .pre-commit-config.yaml | 4 +-- c3/generator/generator.py | 60 ++++++++++++++++++++++++++++----------- 2 files changed, 46 insertions(+), 18 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 63264533..d0b3c942 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: additional_dependencies: [cma==3.0.3, numpy==1.19.5, scipy==1.5.2, - tensorflow==2.4.2, + tensorflow>=2.4.2, tensorflow-probability==0.12.1, - tensorflow-estimator==2.4.0 + tensorflow-estimator>=2.4.0 ] diff --git a/c3/generator/generator.py b/c3/generator/generator.py index 9095015d..2d39541c 100755 --- a/c3/generator/generator.py +++ b/c3/generator/generator.py @@ -17,6 +17,7 @@ from c3.c3objs import hjson_decode, hjson_encode from c3.signal.gates import Instruction from c3.generator.devices import devices as dev_lib +from graphlib import TopologicalSorter class Generator: @@ -45,17 +46,29 @@ def __init__( if devices: self.devices = devices self.chains = {} + self.sorted_chains: dict[str, List[str]] = {} if chains: self.chains = chains self.__check_signal_chains() self.resolution = resolution - self.gen_stacked_signals: dict = None self.callback = callback def __check_signal_chains(self) -> None: for channel, chain in self.chains.items(): signals = 0 - for device_id in chain: + for device_id, sources in chain.items(): + # all source devices need to exist + for dev in sources: + if dev not in self.devices: + raise Exception(f"C3:Error: device {dev} not found.") + + # the expected number of inputs must match the connected devices + if self.devices[device_id].inputs != len(sources): + raise Exception( + f"C3:Error: device {device_id} expects {self.devices[device_id].inputs} inputs, but {len(sources)} found." + ) + + # overall the chain should have exactly 1 output signal signals -= self.devices[device_id].inputs signals += self.devices[device_id].outputs if signals != 1: @@ -65,6 +78,10 @@ def __check_signal_chains(self) -> None: + "' contains unmatched number of inputs and outputs." ) + # bring chain in topological order + sorter = TopologicalSorter(chain) + self.sorted_chains[channel] = list(sorter.static_order()) + def read_config(self, filepath: str) -> None: """ Load a file and parse it to create a Generator object. @@ -123,25 +140,36 @@ def generate_signals(self, instr: Instruction) -> dict: """ gen_signal = {} - gen_stacked_signals: dict = dict() for chan in instr.comps: - signal_stack: List[tf.constant] = [] - gen_stacked_signals[chan] = [] - for dev_id in self.chains[chan]: + chain = self.chains[chan] + + # create list of succeeding devices + successors = {} + for dev_id in chain: + successors[dev_id] = [x for x in chain if dev_id in chain[x]] + + signal_stack: dict[str, tf.constant] = {} + for dev_id in self.sorted_chains[chan]: + # collect inputs + sources = self.chains[chan][dev_id] + inputs = [signal_stack[x] for x in sources] + + # calculate the output and store it in the stack dev = self.devices[dev_id] - inputs = [] - for _input_num in range(dev.inputs): - inputs.append(signal_stack.pop()) - outputs = dev.process(instr, chan, *inputs) - signal_stack.append(outputs) - gen_stacked_signals[chan].append((dev_id, copy.deepcopy(outputs))) + output = dev.process(instr, chan, *inputs) + signal_stack[dev_id] = output + + # remove inputs if they are not needed anymore + for source in sources: + successors[source].remove(dev_id) + if len(successors[source]) < 1: + del signal_stack[source] # call the callback with the current signal if self.callback: - self.callback(chan, dev_id, outputs) - # The stack is reused here, thus we need to deepcopy. - gen_signal[chan] = copy.deepcopy(signal_stack.pop()) - self.gen_stacked_signals = gen_stacked_signals + self.callback(chan, dev_id, output) + + gen_signal[chan] = copy.deepcopy(signal_stack[dev_id]) # Hack to use crosstalk. Will be generalized to a post-processing module. # TODO: Rework of the signal generation for larger chips, similar to qiskit From 8e16ab8a8c647ea338bf6bad30344132a1196387 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Mon, 19 Jul 2021 19:17:14 +0200 Subject: [PATCH 03/80] fix the usage of the new generator chains --- docs/Simulated_Model_Learning.rst | 9 ++++- docs/two_qubits.rst | 18 ++++++++-- examples/Simulated_Model_Learning.ipynb | 13 ++++++-- examples/blackbox_exp.py | 18 ++++++++-- examples/single_qubit_blackbox_exp.py | 9 ++++- examples/two_qubits.ipynb | 18 ++++++++-- test/test_awg.py | 9 ++++- test/test_generator.py | 27 +++++++++++++-- test/test_noise.py | 44 ++++++++++++------------- test/test_tunable_coupler.py | 27 +++++++++++++-- test/test_two_qubits.py | 18 ++++++++-- 11 files changed, 168 insertions(+), 42 deletions(-) diff --git a/docs/Simulated_Model_Learning.rst b/docs/Simulated_Model_Learning.rst index 1abb3465..8996984f 100644 --- a/docs/Simulated_Model_Learning.rst +++ b/docs/Simulated_Model_Learning.rst @@ -370,7 +370,14 @@ Generator ), }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"] + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + } }, ) generator.devices["AWG"].enable_drag_2() diff --git a/docs/two_qubits.rst b/docs/two_qubits.rst index f1b2c70e..54906a84 100644 --- a/docs/two_qubits.rst +++ b/docs/two_qubits.rst @@ -356,8 +356,22 @@ signal chain to each control line. ) }, chains= { - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"] + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + } } ) diff --git a/examples/Simulated_Model_Learning.ipynb b/examples/Simulated_Model_Learning.ipynb index 2ca5bb36..6650429d 100644 --- a/examples/Simulated_Model_Learning.ipynb +++ b/examples/Simulated_Model_Learning.ipynb @@ -729,8 +729,15 @@ " ),\n", " },\n", " chains={\n", - " \"d1\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"]\n", - " },\n", + " \"d1\": {\n", + " \"LO\": [],\n", + " \"AWG\": [],\n", + " \"DigitalToAnalog\": [\"AWG\"],\n", + " \"Response\": [\"DigitalToAnalog\"],\n", + " \"Mixer\": [\"LO\", \"Response\"],\n", + " \"VoltsToHertz\": [\"Mixer\"]\n", + " },\n", + " }\n", ")\n", "generator.devices[\"AWG\"].enable_drag_2()" ] @@ -1824,4 +1831,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file diff --git a/examples/blackbox_exp.py b/examples/blackbox_exp.py index 0b471d53..a5acd13e 100644 --- a/examples/blackbox_exp.py +++ b/examples/blackbox_exp.py @@ -140,8 +140,22 @@ def create_experiment(): ), }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, }, ) generator.devices["awg"].enable_drag_2() diff --git a/examples/single_qubit_blackbox_exp.py b/examples/single_qubit_blackbox_exp.py index 5ed9847f..6a55330b 100644 --- a/examples/single_qubit_blackbox_exp.py +++ b/examples/single_qubit_blackbox_exp.py @@ -90,7 +90,14 @@ def create_experiment(): ), }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"] + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + } }, ) generator.devices["AWG"].enable_drag_2() diff --git a/examples/two_qubits.ipynb b/examples/two_qubits.ipynb index 17cb46ac..dccb7eb2 100644 --- a/examples/two_qubits.ipynb +++ b/examples/two_qubits.ipynb @@ -528,8 +528,22 @@ " )\n", " },\n", " chains= {\n", - " \"d1\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"],\n", - " \"d2\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"]\n", + " \"d1\": {\n", + " \"LO\": [],\n", + " \"AWG\": [],\n", + " \"DigitalToAnalog\": [\"AWG\"],\n", + " \"Response\": [\"DigitalToAnalog\"],\n", + " \"Mixer\": [\"LO\", \"Response\"],\n", + " \"VoltsToHertz\": [\"Mixer\"]\n", + " },\n", + " \"d2\": {\n", + " \"LO\": [],\n", + " \"AWG\": [],\n", + " \"DigitalToAnalog\": [\"AWG\"],\n", + " \"Response\": [\"DigitalToAnalog\"],\n", + " \"Mixer\": [\"LO\", \"Response\"],\n", + " \"VoltsToHertz\": [\"Mixer\"]\n", + " },\n", " }\n", " )" ] diff --git a/test/test_awg.py b/test/test_awg.py index 4f65e154..77180982 100644 --- a/test/test_awg.py +++ b/test/test_awg.py @@ -21,7 +21,14 @@ ), "Mixer": devices.Mixer(name="mixer", inputs=2, outputs=1), }, - chains={"d1": ["LO", "AWG", "DigitalToAnalog", "Mixer"]}, + chains={ + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Mixer": ["LO", "DigitalToAnalog"], + }, + }, ) lo_freq_q1 = 2e9 diff --git a/test/test_generator.py b/test/test_generator.py index 4a3219f3..ba180ffb 100644 --- a/test/test_generator.py +++ b/test/test_generator.py @@ -56,7 +56,14 @@ "VoltsToHertz": v_to_hz, }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"] + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, }, ) @@ -195,8 +202,22 @@ def test_crosstalk() -> None: "crosstalk": xtalk, }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, }, ) RX90p_q1 = Instruction( diff --git a/test/test_noise.py b/test/test_noise.py index 04c3426c..7e55bc49 100644 --- a/test/test_noise.py +++ b/test/test_noise.py @@ -90,15 +90,15 @@ ), }, chains={ - "d1": [ - "LO", - "AWG", - "DigitalToAnalog", - "Response", - "Mixer", - "DCOffset", - "VoltsToHertz", - ], + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "DCOffset": ["Mixer"], + "VoltsToHertz": ["DCOffset"], + }, }, ) @@ -152,19 +152,19 @@ ), }, chains={ - "d1": [ - "LO", - "AWG", - "AWGNoise", - "DigitalToAnalog", - "Response", - "Highpass", - "Mixer", - "DCNoise", - "PinkNoise", - "DCOffset", - "VoltsToHertz", - ], + "d1": { + "LO": [], + "AWG": [], + "AWGNoise": ["AWG"], + "DigitalToAnalog": ["AWGNoise"], + "Response": ["DigitalToAnalog"], + "Highpass": ["Response"], + "Mixer": ["LO", "Highpass"], + "DCNoise": ["Mixer"], + "PinkNoise": ["DCNoise"], + "DCOffset": ["PinkNoise"], + "VoltsToHertz": ["DCOffset"], + }, }, ) diff --git a/test/test_tunable_coupler.py b/test/test_tunable_coupler.py index 85aebe37..0e902b92 100644 --- a/test/test_tunable_coupler.py +++ b/test/test_tunable_coupler.py @@ -190,9 +190,30 @@ generator = Gnr( devices=device_dict, chains={ - "TC": ["lo", "awg", "dac", "resp", "mixer", "fluxbias"], - "Q1": ["lo", "awg", "dac", "resp", "mixer", "v2hz"], - "Q2": ["lo", "awg", "dac", "resp", "mixer", "v2hz"], + "TC": { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "resp"], + "fluxbias": ["mixer"], + }, + "Q1": { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "resp"], + "v2hz": ["mixer"], + }, + "Q2": { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "resp"], + "v2hz": ["mixer"], + }, }, ) diff --git a/test/test_two_qubits.py b/test/test_two_qubits.py index 036f4278..2d5c9c66 100644 --- a/test/test_two_qubits.py +++ b/test/test_two_qubits.py @@ -151,8 +151,22 @@ ), }, chains={ - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"], + }, }, ) From 863469e68dedbd02f7c2d24e139ce46a4955d18a Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Thu, 29 Jul 2021 16:30:47 +0200 Subject: [PATCH 04/80] changelog --- CHANGELOG.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6526f6a9..cd6465a4 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,8 @@ This Changelog tracks all past changes to this project as well as details about - `fixed` for any bug fixes. - `security` in case of vulnerabilities. +- `changed` The generator now can handle any list of devices that forms a directed graph #129 + ## Version `1.3` - 20 Jul 2021 ### Summary From 159254c26f4a68d9ac14c01e9a87e91f4b265e40 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Mon, 2 Aug 2021 22:46:18 +0200 Subject: [PATCH 05/80] replace graphlib with custom topological sorting --- .pre-commit-config.yaml | 4 ++-- c3/generator/generator.py | 49 ++++++++++++++++++++++++++++++++++----- 2 files changed, 45 insertions(+), 8 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index d0b3c942..eb05022c 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: additional_dependencies: [cma==3.0.3, numpy==1.19.5, scipy==1.5.2, - tensorflow>=2.4.2, + tensorflow==2.4.2, tensorflow-probability==0.12.1, - tensorflow-estimator>=2.4.0 + tensorflow-estimator==2.4.0 ] diff --git a/c3/generator/generator.py b/c3/generator/generator.py index 2d39541c..9e955a78 100755 --- a/c3/generator/generator.py +++ b/c3/generator/generator.py @@ -10,14 +10,13 @@ """ import copy -from typing import List, Callable +from typing import List, Callable, Dict import hjson import numpy as np import tensorflow as tf from c3.c3objs import hjson_decode, hjson_encode from c3.signal.gates import Instruction from c3.generator.devices import devices as dev_lib -from graphlib import TopologicalSorter class Generator: @@ -46,7 +45,7 @@ def __init__( if devices: self.devices = devices self.chains = {} - self.sorted_chains: dict[str, List[str]] = {} + self.sorted_chains: Dict[str, List[str]] = {} if chains: self.chains = chains self.__check_signal_chains() @@ -79,8 +78,46 @@ def __check_signal_chains(self) -> None: ) # bring chain in topological order - sorter = TopologicalSorter(chain) - self.sorted_chains[channel] = list(sorter.static_order()) + self.sorted_chains[channel] = self.__topological_ordering( + self.chains[channel] + ) + + def __topological_ordering(self, predecessors: Dict[str, List[str]]) -> List[str]: + """ + Computes the topological ordering of a directed acyclic graph. + + Parameters + ---------- + predecessors : dict + list of preceding nodes for each node + + Returns + ------- + a list of all nodes in topological ordering + + Raises + ------ + ValueError + if the graph contains a cycle + """ + stack = [x for x in predecessors if len(predecessors[x]) == 0] + num_sources = {node: len(predecessors[node]) for node in predecessors} + successors = {} + for node in predecessors: + successors[node] = [x for x in predecessors if node in predecessors[x]] + ordered = [] + + while stack: + src = stack.pop() + for node in successors[src]: + num_sources[node] -= 1 + if num_sources[node] == 0: + stack.append(node) + ordered.append(src) + + if len(ordered) != len(successors): + raise Exception("C3:ERROR: Device chain contains a cycle") + return ordered def read_config(self, filepath: str) -> None: """ @@ -148,7 +185,7 @@ def generate_signals(self, instr: Instruction) -> dict: for dev_id in chain: successors[dev_id] = [x for x in chain if dev_id in chain[x]] - signal_stack: dict[str, tf.constant] = {} + signal_stack: Dict[str, tf.constant] = {} for dev_id in self.sorted_chains[chan]: # collect inputs sources = self.chains[chan][dev_id] From 67f502bf7c4c9de4ef578e5215f134273b1cf7b7 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Tue, 3 Aug 2021 09:50:49 +0200 Subject: [PATCH 06/80] fix the generator in remaining tests --- .pre-commit-config.yaml | 2 +- examples/generator.cfg | 20 +++++++++++++++++--- test/generator.cfg | 20 +++++++++++++++++--- test/generator2.cfg | 20 +++++++++++++++++--- test/test_awg.py | 1 - test/test_transmon_expanded.py | 16 ++++++++++++++-- 6 files changed, 66 insertions(+), 13 deletions(-) diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index eb05022c..63264533 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -16,7 +16,7 @@ repos: additional_dependencies: [cma==3.0.3, numpy==1.19.5, scipy==1.5.2, - tensorflow==2.4.2, + tensorflow==2.4.2, tensorflow-probability==0.12.1, tensorflow-estimator==2.4.0 ] diff --git a/examples/generator.cfg b/examples/generator.cfg index e72a46f0..4ade5e88 100644 --- a/examples/generator.cfg +++ b/examples/generator.cfg @@ -47,7 +47,21 @@ } }, "Chains": { - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - } + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + } } diff --git a/test/generator.cfg b/test/generator.cfg index e72a46f0..66312b47 100644 --- a/test/generator.cfg +++ b/test/generator.cfg @@ -47,7 +47,21 @@ } }, "Chains": { - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - } + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + } } diff --git a/test/generator2.cfg b/test/generator2.cfg index f30af1c4..e6c95c81 100644 --- a/test/generator2.cfg +++ b/test/generator2.cfg @@ -47,7 +47,21 @@ } }, "Chains": { - "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], - } + "d1": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + "d2": { + "LO": [], + "AWG": [], + "DigitalToAnalog": ["AWG"], + "Response": ["DigitalToAnalog"], + "Mixer": ["LO", "Response"], + "VoltsToHertz": ["Mixer"] + }, + } } diff --git a/test/test_awg.py b/test/test_awg.py index 77180982..53f3b459 100644 --- a/test/test_awg.py +++ b/test/test_awg.py @@ -63,7 +63,6 @@ def test_AWG_phase_shift() -> None: sigs = generator.generate_signals(rectangle) correct_signal = np.cos(2 * np.pi * lo_freq_q1 * sigs["d1"]["ts"] + phase * np.pi) print(sigs["d1"]["values"]) - print(generator.gen_stacked_signals) np.testing.assert_allclose( sigs["d1"]["values"].numpy(), correct_signal, diff --git a/test/test_transmon_expanded.py b/test/test_transmon_expanded.py index 63364057..678e7146 100644 --- a/test/test_transmon_expanded.py +++ b/test/test_transmon_expanded.py @@ -92,8 +92,20 @@ generator = Generator( devices=device_dict, chains={ - "Qubit1": ["lo", "awg", "dac", "resp", "mixer"], - "Qubit2": ["lo", "awg", "dac", "resp", "mixer"], + "Qubit1": { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "awg"], + }, + "Qubit2": { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "awg"], + }, }, ) From 035f9c9aa6d4025a769a5a80f85ea38acdd111b7 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 11:15:43 +0200 Subject: [PATCH 07/80] Deleted empty file. --- c3/utils/parsers.py | 0 1 file changed, 0 insertions(+), 0 deletions(-) delete mode 100755 c3/utils/parsers.py diff --git a/c3/utils/parsers.py b/c3/utils/parsers.py deleted file mode 100755 index e69de29b..00000000 From ad18e04d135f1228774ad7816735bd4b51696aad Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 11:16:21 +0200 Subject: [PATCH 08/80] Checking if called by main. --- c3/utils/log_reader.py | 52 +++++++++++++++++++----------------------- 1 file changed, 24 insertions(+), 28 deletions(-) diff --git a/c3/utils/log_reader.py b/c3/utils/log_reader.py index 2b045bc0..d1a28bff 100755 --- a/c3/utils/log_reader.py +++ b/c3/utils/log_reader.py @@ -9,21 +9,7 @@ from rich.table import Table -parser = argparse.ArgumentParser() -parser.add_argument("log_file") -parser.add_argument("-w", "--watch", action="store_true") -args = parser.parse_args() - -log = None - -try: - with open(args.log_file) as file: - log = hjson.load(file, object_pairs_hook=hjson_decode) -except FileNotFoundError: - print("Logfile not found.") - - -def show_table(): +def show_table(log, console) -> None: if log: opt_map = log["opt_map"] optim_status = log["optim_status"] @@ -35,19 +21,16 @@ def show_table(): table.add_column("Parameter") table.add_column("Value", justify="right") table.add_column("Gradient", justify="right") - for ii in range(len(opt_map)): - equiv_ids = opt_map[ii] + for ii, equiv_ids in enumerate(opt_map): par = params[ii] grad = grads[ii] par = num3str(par) grad = num3str(grad) par_id = equiv_ids[0] - nice_id = "-".join(par_id) - table.add_row(nice_id, par + units[ii], grad + units[ii]) + table.add_row(par_id, par + units[ii], grad + units[ii]) if len(equiv_ids) > 1: for par_id in equiv_ids[1:]: - nice_id = "-".join(par_id) - table.add_row(nice_id, "''", "''") + table.add_row(par_id, "''", "''") console.clear() print( @@ -56,10 +39,23 @@ def show_table(): console.print(table) -console = Console() -if args.watch: - while True: - show_table() - time.sleep(5) -else: - show_table() +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("log_file") + parser.add_argument("-w", "--watch", action="store_true") + args = parser.parse_args() + log = None + + try: + with open(args.log_file) as file: + log = hjson.load(file, object_pairs_hook=hjson_decode) + except FileNotFoundError: + print("Logfile not found.") + + console = Console() + if args.watch: + while True: + show_table(log, console) + time.sleep(5) + else: + show_table(log, console) From 810dda7709086e4f335fbea315e81f317470db29 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 11:18:00 +0200 Subject: [PATCH 09/80] Added test for log reader. --- test/test_cli_utils.py | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) create mode 100644 test/test_cli_utils.py diff --git a/test/test_cli_utils.py b/test/test_cli_utils.py new file mode 100644 index 00000000..b796b82e --- /dev/null +++ b/test/test_cli_utils.py @@ -0,0 +1,23 @@ +""" +Tests for command line utilities. +""" + +import pytest +import hjson +from rich.console import Console + +from c3.c3objs import hjson_decode +from c3.utils.log_reader import show_table + +SAMPLE_LOG = "test/sample_optim_log.log" + + +@pytest.mark.unit +def test_log_viewer(): + """ + Check that the log is read and processed without error. This does not check if + the output is correct. + """ + console = Console() + with open(SAMPLE_LOG) as logfile: + show_table(hjson.load(logfile, object_pairs_hook=hjson_decode), console) From 0bf3c923e49ee4fcadeedf78bf5f13ab22cfc2dd Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 12:06:01 +0200 Subject: [PATCH 10/80] Docstring, default cli args and description. --- c3/utils/log_reader.py | 44 ++++++++++++++++++++++++++++-------------- 1 file changed, 29 insertions(+), 15 deletions(-) diff --git a/c3/utils/log_reader.py b/c3/utils/log_reader.py index d1a28bff..01a4f079 100755 --- a/c3/utils/log_reader.py +++ b/c3/utils/log_reader.py @@ -1,15 +1,25 @@ -#!/usr/bin/python -u +#!/usr/bin/env python3 import time import argparse import hjson -from c3.c3objs import hjson_decode +from typing import Any, Dict + from c3.utils.utils import num3str from rich.console import Console from rich.table import Table -def show_table(log, console) -> None: +def show_table(log: Dict[str, Any], console: Console) -> None: + """Generate a rich table from an optimization status and display it on the console. + + Parameters + ---------- + log : Dict + Dictionary read from a json log file containing a c3-toolset optimization status. + console : Console + Rich console for output. + """ if log: opt_map = log["opt_map"] optim_status = log["optim_status"] @@ -42,20 +52,24 @@ def show_table(log, console) -> None: if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("log_file") - parser.add_argument("-w", "--watch", action="store_true") + parser.add_argument( + "-w", + "--watch", + type=int, + default=0, + help="Update the table every WATCH seconds.", + ) args = parser.parse_args() - log = None try: with open(args.log_file) as file: - log = hjson.load(file, object_pairs_hook=hjson_decode) - except FileNotFoundError: - print("Logfile not found.") - - console = Console() - if args.watch: - while True: + log = hjson.load(file) + console = Console() + if args.watch: + while True: + show_table(log, console) + time.sleep(args.watch) + else: show_table(log, console) - time.sleep(5) - else: - show_table(log, console) + except FileNotFoundError: + print("Logfile not found. Quiting...") From 05b893b7a338d40348bf848cd84f830795c96413 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 12:06:31 +0200 Subject: [PATCH 11/80] Log reader section added. --- docs/index.rst | 1 + docs/log_reader.rst | 48 +++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 49 insertions(+) create mode 100644 docs/log_reader.rst diff --git a/docs/index.rst b/docs/index.rst index 43c71c47..21da5fa8 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -32,6 +32,7 @@ When combined in sequence, these three procedures represent a recipe for system optimal_control Simulated_calibration Simulated_Model_Learning + log_reader c3_qiskit_example c3 diff --git a/docs/log_reader.rst b/docs/log_reader.rst new file mode 100644 index 00000000..35405c5e --- /dev/null +++ b/docs/log_reader.rst @@ -0,0 +1,48 @@ +Logs and current optimization status +==================================== + +During optimizations (optimal control, calibration, model learning), a +current best point is stored in the log folder to monitor progress. +Called on a log file it will print a +`rich `__ table of the current +status. With the ``-w`` or ``-- watch`` options the table will keep +updating. + +.. code:: bash + + c3/utils/log_reader.py -h + + +.. code-block:: + + usage: log_reader.py [-h] [-w WATCH] log_file + + positional arguments: + log_file + + optional arguments: + -h, --help show this help message and exit + -w WATCH, --watch WATCH + Update the table every WATCH seconds. + + +Using the example log from the test folder: + +.. code:: bash + + c3/utils/log_reader.py test/sample_optim_log.log + + +.. parsed-literal:: + + Optimization reached 0.00462 at Tue Aug 17 15:28:09 2021 + + ┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━┓ + ┃ Parameter ┃ Value ┃ Gradient ┃ + ┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━┩ + │ rx90p[0]-d1-gauss-amp │ 497.311 mV │ 18.720 mV │ + │ rx90p[0]-d1-gauss-freq_offset │ -52.998 MHz 2pi │ -414.237 µHz 2pi │ + │ rx90p[0]-d1-gauss-xy_angle │ -47.409 mrad │ 2.904 mrad │ + │ rx90p[0]-d1-gauss-delta │ -1.077 │ 6.648 m │ + └───────────────────────────────┴─────────────────┴──────────────────┘ + From a2771faf8f2382956f69d842822dae00972fe650 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 18 Aug 2021 13:42:58 +0200 Subject: [PATCH 12/80] Added sample log. --- docs/log_reader.rst | 2 +- test/sample_optim_log.c3log | 1 + test/test_cli_utils.py | 2 +- 3 files changed, 3 insertions(+), 2 deletions(-) create mode 100644 test/sample_optim_log.c3log diff --git a/docs/log_reader.rst b/docs/log_reader.rst index 35405c5e..8878aa1d 100644 --- a/docs/log_reader.rst +++ b/docs/log_reader.rst @@ -30,7 +30,7 @@ Using the example log from the test folder: .. code:: bash - c3/utils/log_reader.py test/sample_optim_log.log + c3/utils/log_reader.py test/sample_optim_log.c3log .. parsed-literal:: diff --git a/test/sample_optim_log.c3log b/test/sample_optim_log.c3log new file mode 100644 index 00000000..b664d6ba --- /dev/null +++ b/test/sample_optim_log.c3log @@ -0,0 +1 @@ +{"opt_map": [["rx90p[0]-d1-gauss-amp"], ["rx90p[0]-d1-gauss-freq_offset"], ["rx90p[0]-d1-gauss-xy_angle"], ["rx90p[0]-d1-gauss-delta"]], "units": ["V", "Hz 2pi", "rad", ""], "optim_status": {"params": [0.49731057256150457, -52997604.24565414, -0.0474089606329513, -1.0765842275871154], "goal": 0.004623751716391289, "time": "Tue Aug 17 15:28:09 2021", "gradient": [0.018719753658557353, -0.00041423747880565335, 0.0029041996681835715, 0.006648118709775015]}} diff --git a/test/test_cli_utils.py b/test/test_cli_utils.py index b796b82e..21513cce 100644 --- a/test/test_cli_utils.py +++ b/test/test_cli_utils.py @@ -9,7 +9,7 @@ from c3.c3objs import hjson_decode from c3.utils.log_reader import show_table -SAMPLE_LOG = "test/sample_optim_log.log" +SAMPLE_LOG = "test/sample_optim_log.c3log" @pytest.mark.unit From 32b0d931ed87fe59a7be4d26a4cf4e5a0d4ddcda Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 7 Sep 2021 16:18:58 +0200 Subject: [PATCH 13/80] Saving optim points in human format. --- c3/optimizers/optimizer.py | 2 +- test/best_point_open_loop.c3log | 36 +++++++++++++++++++++++++++++++++ test/test_two_qubits.py | 5 +++++ 3 files changed, 42 insertions(+), 1 deletion(-) create mode 100644 test/best_point_open_loop.c3log diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 2146cb23..f8428e54 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -177,7 +177,7 @@ def log_parameters(self) -> None: "units": self.pmap.get_opt_units(), "optim_status": self.optim_status, } - best_point.write(hjson.dumpsJSON(best_dict, default=hjson_encode)) + best_point.write(hjson.dumps(best_dict, default=hjson_encode)) best_point.write("\n") if self.store_unitaries: self.exp.store_Udict(self.optim_status["goal"]) diff --git a/test/best_point_open_loop.c3log b/test/best_point_open_loop.c3log new file mode 100644 index 00000000..3850b753 --- /dev/null +++ b/test/best_point_open_loop.c3log @@ -0,0 +1,36 @@ +{ + opt_map: + [ + [ + rx90p[0]-d1-gauss-amp + ] + [ + rx90p[0]-d1-gauss-freq_offset + ] + [ + rx90p[0]-d1-gauss-xy_angle + ] + [ + rx90p[0]-d1-gauss-delta + ] + ] + units: + [ + V + Hz 2pi + rad + "" + ] + optim_status: + { + params: + [ + 0.4592487865228791 + -53602477.20323461 + -0.049196719488305174 + -1.2413858431966456 + ] + goal: 0.0005627262925752552 + time: Tue Sep 7 16:10:19 2021 + } +} diff --git a/test/test_two_qubits.py b/test/test_two_qubits.py index 036f4278..e09432b1 100644 --- a/test/test_two_qubits.py +++ b/test/test_two_qubits.py @@ -348,6 +348,11 @@ def test_propagation() -> None: almost_equal(propagator, test_data["propagator"]) +def test_init_point() -> None: + """Check that a previous best point can be loaded as an initial point.""" + opt.load_best("test/best_point_open_loop.c3log") + + @pytest.mark.slow @pytest.mark.tensorflow @pytest.mark.optimizers From 151da06245f276dd70cf9d94fb0fb7432336fbbc Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 7 Sep 2021 16:31:41 +0200 Subject: [PATCH 14/80] Made gradient optional in log reader. --- c3/utils/log_reader.py | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/c3/utils/log_reader.py b/c3/utils/log_reader.py index 01a4f079..e58fd6a6 100755 --- a/c3/utils/log_reader.py +++ b/c3/utils/log_reader.py @@ -25,22 +25,27 @@ def show_table(log: Dict[str, Any], console: Console) -> None: optim_status = log["optim_status"] units = log["units"] params = optim_status["params"] - grads = optim_status["gradient"] + grads = optim_status.pop("gradient", None) table = Table(show_header=True, header_style="bold magenta") table.add_column("Parameter") table.add_column("Value", justify="right") - table.add_column("Gradient", justify="right") + if grads is not None: + table.add_column("Gradient", justify="right") for ii, equiv_ids in enumerate(opt_map): par = params[ii] - grad = grads[ii] par = num3str(par) - grad = num3str(grad) par_id = equiv_ids[0] - table.add_row(par_id, par + units[ii], grad + units[ii]) - if len(equiv_ids) > 1: - for par_id in equiv_ids[1:]: - table.add_row(par_id, "''", "''") + if grads is not None: + table.add_row(par_id, par + units[ii], num3str(grads[ii]) + units[ii]) + if len(equiv_ids) > 1: + for par_id in equiv_ids[1:]: + table.add_row(par_id, "''", "''") + else: + table.add_row(par_id, par + units[ii]) + if len(equiv_ids) > 1: + for par_id in equiv_ids[1:]: + table.add_row(par_id, "''") console.clear() print( From 44a95691ec3ffb14c0ba19aa5884a4d0312db3bf Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 8 Sep 2021 11:34:02 +0200 Subject: [PATCH 15/80] Replaced ast call with hjson to load log. --- examples/Simulated_Model_Learning.ipynb | 1148 +++++++++++------------ 1 file changed, 526 insertions(+), 622 deletions(-) diff --git a/examples/Simulated_Model_Learning.ipynb b/examples/Simulated_Model_Learning.ipynb index 2ca5bb36..49398dd1 100644 --- a/examples/Simulated_Model_Learning.ipynb +++ b/examples/Simulated_Model_Learning.ipynb @@ -2,41 +2,28 @@ "cells": [ { "cell_type": "markdown", - "id": "5bea3050", - "metadata": {}, "source": [ "# Model Learning on Dataset from a Simulated Experiment" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "019b2f19", - "metadata": {}, "source": [ "In this notebook, we will use a dataset from a simulated experiment, more specifically, the `Simulated_calibration.ipynb` example notebook and perform Model Learning on a simple 1 qubit model." - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "17cfa324", - "metadata": {}, "source": [ "### Imports" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 1, - "id": "213a962b", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:16.689570Z", - "iopub.status.busy": "2021-06-30T06:29:16.684069Z", - "iopub.status.idle": "2021-06-30T06:29:24.343696Z", - "shell.execute_reply": "2021-06-30T06:29:24.343985Z" - } - }, - "outputs": [], "source": [ "import pickle\n", "from pprint import pprint\n", @@ -44,6 +31,7 @@ "import numpy as np\n", "import os\n", "import ast\n", + "import hjson\n", "import pandas as pd\n", "\n", "from c3.model import Model as Mdl\n", @@ -60,28 +48,38 @@ "import c3.libraries.tasks as tasks\n", "from c3.optimizers.modellearning import ModelLearning\n", "from c3.optimizers.sensitivity import Sensitivity" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:16.689570Z", + "iopub.status.busy": "2021-06-30T06:29:16.684069Z", + "iopub.status.idle": "2021-06-30T06:29:24.343696Z", + "shell.execute_reply": "2021-06-30T06:29:24.343985Z" + } + } }, { "cell_type": "markdown", - "id": "f9a0b13e", - "metadata": {}, "source": [ "## The Dataset" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "9cf9b477", - "metadata": {}, "source": [ "We first take a look below at the dataset and its properties. To explore more details about how the dataset is generated, please refer to the `Simulated_calibration.ipynb` example notebook." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 2, - "id": "82466603", + "source": [ + "DATAFILE_PATH = \"data/small_dataset.pkl\"" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.346337Z", @@ -89,16 +87,16 @@ "iopub.status.idle": "2021-06-30T06:29:24.347420Z", "shell.execute_reply": "2021-06-30T06:29:24.347670Z" } - }, - "outputs": [], - "source": [ - "DATAFILE_PATH = \"data/small_dataset.pkl\"" - ] + } }, { "cell_type": "code", "execution_count": 3, - "id": "91d77816", + "source": [ + "with open(DATAFILE_PATH, \"rb+\") as file:\n", + " data = pickle.load(file)" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.349797Z", @@ -106,63 +104,51 @@ "iopub.status.idle": "2021-06-30T06:29:24.403393Z", "shell.execute_reply": "2021-06-30T06:29:24.403893Z" } - }, - "outputs": [], - "source": [ - "with open(DATAFILE_PATH, \"rb+\") as file:\n", - " data = pickle.load(file)" - ] + } }, { "cell_type": "code", "execution_count": 4, - "id": "510af3db", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.414664Z", - "iopub.status.busy": "2021-06-30T06:29:24.414128Z", - "iopub.status.idle": "2021-06-30T06:29:24.416895Z", - "shell.execute_reply": "2021-06-30T06:29:24.417303Z" - } - }, + "source": [ + "data.keys()" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['seqs_grouped_by_param_set', 'opt_map'])" ] }, - "execution_count": 4, "metadata": {}, - "output_type": "execute_result" + "execution_count": 4 } ], - "source": [ - "data.keys()" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.414664Z", + "iopub.status.busy": "2021-06-30T06:29:24.414128Z", + "iopub.status.idle": "2021-06-30T06:29:24.416895Z", + "shell.execute_reply": "2021-06-30T06:29:24.417303Z" + } + } }, { "cell_type": "markdown", - "id": "d023f8bc", - "metadata": {}, "source": [ "Since this dataset was obtained from an ORBIT ([arXiv:1403.0035](https://arxiv.org/abs/1403.0035)) calibration experiment, we have the `opt_map` which will tell us about the gateset parameters being optimized." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 5, - "id": "99b5a68c", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.421146Z", - "iopub.status.busy": "2021-06-30T06:29:24.420618Z", - "iopub.status.idle": "2021-06-30T06:29:24.423293Z", - "shell.execute_reply": "2021-06-30T06:29:24.423687Z" - } - }, + "source": [ + "data[\"opt_map\"]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "[['rx90p[0]-d1-gauss-amp',\n", @@ -180,37 +166,42 @@ " ['id[0]-d1-carrier-framechange']]" ] }, - "execution_count": 5, "metadata": {}, - "output_type": "execute_result" + "execution_count": 5 } ], - "source": [ - "data[\"opt_map\"]" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.421146Z", + "iopub.status.busy": "2021-06-30T06:29:24.420618Z", + "iopub.status.idle": "2021-06-30T06:29:24.423293Z", + "shell.execute_reply": "2021-06-30T06:29:24.423687Z" + } + } }, { "cell_type": "markdown", - "id": "dd6923c6", - "metadata": {}, "source": [ "This `opt_map` implies the calibration experiment focussed on optimizing \n", "the amplitude, delta and frequency offset of the gaussian pulse, along \n", "with the framechange angle" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "4421b86d", - "metadata": {}, "source": [ "Now onto the actual measurement data from the experiment runs" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 6, - "id": "0e007bbe", + "source": [ + "seqs_data = data[\"seqs_grouped_by_param_set\"]" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.426544Z", @@ -218,62 +209,58 @@ "iopub.status.idle": "2021-06-30T06:29:24.428263Z", "shell.execute_reply": "2021-06-30T06:29:24.427861Z" } - }, - "outputs": [], - "source": [ - "seqs_data = data[\"seqs_grouped_by_param_set\"]" - ] + } }, { "cell_type": "markdown", - "id": "86849ace", - "metadata": {}, "source": [ "**How many experiment runs do we have?**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 7, - "id": "6fe6715c", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.431169Z", - "iopub.status.busy": "2021-06-30T06:29:24.430725Z", - "iopub.status.idle": "2021-06-30T06:29:24.433094Z", - "shell.execute_reply": "2021-06-30T06:29:24.433444Z" - } - }, + "source": [ + "len(seqs_data)" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "41" ] }, - "execution_count": 7, "metadata": {}, - "output_type": "execute_result" + "execution_count": 7 } ], - "source": [ - "len(seqs_data)" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.431169Z", + "iopub.status.busy": "2021-06-30T06:29:24.430725Z", + "iopub.status.idle": "2021-06-30T06:29:24.433094Z", + "shell.execute_reply": "2021-06-30T06:29:24.433444Z" + } + } }, { "cell_type": "markdown", - "id": "2acdf376", - "metadata": {}, "source": [ "**What does the data from each experiment look like?**\n", "\n", "We take a look at the first data point" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 8, - "id": "45a3b9b0", + "source": [ + "example_data_point = seqs_data[0]" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.436247Z", @@ -281,114 +268,99 @@ "iopub.status.idle": "2021-06-30T06:29:24.437588Z", "shell.execute_reply": "2021-06-30T06:29:24.437939Z" } - }, - "outputs": [], - "source": [ - "example_data_point = seqs_data[0]" - ] + } }, { "cell_type": "code", "execution_count": 9, - "id": "8ee97785", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.440859Z", - "iopub.status.busy": "2021-06-30T06:29:24.440410Z", - "iopub.status.idle": "2021-06-30T06:29:24.443139Z", - "shell.execute_reply": "2021-06-30T06:29:24.442720Z" - } - }, + "source": [ + "example_data_point.keys()" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['params', 'seqs', 'results', 'results_std', 'shots'])" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "execution_count": 9 } ], - "source": [ - "example_data_point.keys()" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.440859Z", + "iopub.status.busy": "2021-06-30T06:29:24.440410Z", + "iopub.status.idle": "2021-06-30T06:29:24.443139Z", + "shell.execute_reply": "2021-06-30T06:29:24.442720Z" + } + } }, { "cell_type": "markdown", - "id": "af899d42", - "metadata": {}, "source": [ "These `keys` are useful in understanding the structure of the dataset. We look at them one by one." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 10, - "id": "229e8dab", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.447311Z", - "iopub.status.busy": "2021-06-30T06:29:24.446850Z", - "iopub.status.idle": "2021-06-30T06:29:24.450577Z", - "shell.execute_reply": "2021-06-30T06:29:24.450890Z" - } - }, + "source": [ + "example_data_point[\"params\"]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "[450.000 mV, -1.000 , -50.500 MHz 2pi, 4.084 rad]" ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "execution_count": 10 } ], - "source": [ - "example_data_point[\"params\"]" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.447311Z", + "iopub.status.busy": "2021-06-30T06:29:24.446850Z", + "iopub.status.idle": "2021-06-30T06:29:24.450577Z", + "shell.execute_reply": "2021-06-30T06:29:24.450890Z" + } + } }, { "cell_type": "markdown", - "id": "b9731367", - "metadata": {}, "source": [ "These are the parameters for our parameterised gateset, for the first experiment run. They correspond to the optimization parameters we previously discussed. " - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "d2c8a453", - "metadata": {}, "source": [ "The `seqs` key stores the sequence of gates that make up this ORBIT calibration experiment. Each ORBIT sequence consists of a set of gates, followed by a measurement operation. This is then repeated for some `n` number of shots (eg, `1000` in this case) and we only store the averaged result along with the standard deviation of these readout shots. Each experiment in turn consists of a number of these ORBIT sequences. The terms *sequence*, *set* and *experiment* are used somewhat loosely here, so we show below what these look like." - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "bdb8ea1e", - "metadata": {}, "source": [ "**A single ORBIT sequence**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 11, - "id": "2d3a7265", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.454002Z", - "iopub.status.busy": "2021-06-30T06:29:24.453650Z", - "iopub.status.idle": "2021-06-30T06:29:24.455626Z", - "shell.execute_reply": "2021-06-30T06:29:24.455901Z" - } - }, + "source": [ + "example_data_point[\"seqs\"][0]" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "['ry90p[0]',\n", @@ -419,99 +391,104 @@ " 'rx90p[0]']" ] }, - "execution_count": 11, "metadata": {}, - "output_type": "execute_result" + "execution_count": 11 } ], - "source": [ - "example_data_point[\"seqs\"][0]" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.454002Z", + "iopub.status.busy": "2021-06-30T06:29:24.453650Z", + "iopub.status.idle": "2021-06-30T06:29:24.455626Z", + "shell.execute_reply": "2021-06-30T06:29:24.455901Z" + } + } }, { "cell_type": "markdown", - "id": "194df3e9", - "metadata": {}, "source": [ "**Total number of ORBIT sequences in an experiment**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 12, - "id": "d5cfef36", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.458262Z", - "iopub.status.busy": "2021-06-30T06:29:24.457903Z", - "iopub.status.idle": "2021-06-30T06:29:24.459781Z", - "shell.execute_reply": "2021-06-30T06:29:24.460029Z" - } - }, + "source": [ + "len(example_data_point[\"seqs\"])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "20" ] }, - "execution_count": 12, "metadata": {}, - "output_type": "execute_result" + "execution_count": 12 } ], - "source": [ - "len(example_data_point[\"seqs\"])" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.458262Z", + "iopub.status.busy": "2021-06-30T06:29:24.457903Z", + "iopub.status.idle": "2021-06-30T06:29:24.459781Z", + "shell.execute_reply": "2021-06-30T06:29:24.460029Z" + } + } }, { "cell_type": "markdown", - "id": "6b78defe", - "metadata": {}, "source": [ "**Total number of Measurement results**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 13, - "id": "9f5081a8", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.462194Z", - "iopub.status.busy": "2021-06-30T06:29:24.461850Z", - "iopub.status.idle": "2021-06-30T06:29:24.463711Z", - "shell.execute_reply": "2021-06-30T06:29:24.463958Z" - } - }, + "source": [ + "len(example_data_point[\"results\"])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "20" ] }, - "execution_count": 13, "metadata": {}, - "output_type": "execute_result" + "execution_count": 13 } ], - "source": [ - "len(example_data_point[\"results\"])" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.462194Z", + "iopub.status.busy": "2021-06-30T06:29:24.461850Z", + "iopub.status.idle": "2021-06-30T06:29:24.463711Z", + "shell.execute_reply": "2021-06-30T06:29:24.463958Z" + } + } }, { "cell_type": "markdown", - "id": "95a50901", - "metadata": {}, "source": [ "**The measurement results and the standard deviation look like this**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 14, - "id": "757e091a", + "source": [ + "example_results = [\n", + " (example_data_point[\"results\"][i], example_data_point[\"results_std\"][i])\n", + " for i in range(len(example_data_point[\"results\"]))\n", + "]" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.466251Z", @@ -519,31 +496,18 @@ "iopub.status.idle": "2021-06-30T06:29:24.467336Z", "shell.execute_reply": "2021-06-30T06:29:24.467583Z" } - }, - "outputs": [], - "source": [ - "example_results = [\n", - " (example_data_point[\"results\"][i], example_data_point[\"results_std\"][i])\n", - " for i in range(len(example_data_point[\"results\"]))\n", - "]" - ] + } }, { "cell_type": "code", "execution_count": 15, - "id": "28f4717b", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.470224Z", - "iopub.status.busy": "2021-06-30T06:29:24.469909Z", - "iopub.status.idle": "2021-06-30T06:29:24.473051Z", - "shell.execute_reply": "2021-06-30T06:29:24.472782Z" - } - }, + "source": [ + "pprint(example_results)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "[([0.745], [0.013783141876945182]),\n", " ([0.213], [0.012947239087929134]),\n", @@ -568,47 +532,39 @@ ] } ], - "source": [ - "pprint(example_results)" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.470224Z", + "iopub.status.busy": "2021-06-30T06:29:24.469909Z", + "iopub.status.idle": "2021-06-30T06:29:24.473051Z", + "shell.execute_reply": "2021-06-30T06:29:24.472782Z" + } + } }, { "cell_type": "markdown", - "id": "6fd05bec", - "metadata": {}, "source": [ "## The Model for Model Learning" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "c5e301c6", - "metadata": {}, "source": [ "An initial model needs to be provided, which we refine by fitting to our calibration data. We do this below. If you want to learn more about what the various components of the model mean, please refer back to the `two_qubits.ipynb` notebook or the documentation." - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "71010459", - "metadata": {}, "source": [ "### Define Constants" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 16, - "id": "21424f7d", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.475726Z", - "iopub.status.busy": "2021-06-30T06:29:24.475401Z", - "iopub.status.idle": "2021-06-30T06:29:24.476822Z", - "shell.execute_reply": "2021-06-30T06:29:24.477068Z" - } - }, - "outputs": [], "source": [ "lindblad = False\n", "dressed = True\n", @@ -622,29 +578,27 @@ "awg_res = 2e9\n", "sideband = 50e6\n", "lo_freq = 5e9 + sideband" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.475726Z", + "iopub.status.busy": "2021-06-30T06:29:24.475401Z", + "iopub.status.idle": "2021-06-30T06:29:24.476822Z", + "shell.execute_reply": "2021-06-30T06:29:24.477068Z" + } + } }, { "cell_type": "markdown", - "id": "6c6d17dc", - "metadata": {}, "source": [ "### Model" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 17, - "id": "dc7d57d0", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.480926Z", - "iopub.status.busy": "2021-06-30T06:29:24.480595Z", - "iopub.status.idle": "2021-06-30T06:29:24.488368Z", - "shell.execute_reply": "2021-06-30T06:29:24.488635Z" - } - }, - "outputs": [], "source": [ "q1 = chip.Qubit(\n", " name=\"Q1\",\n", @@ -682,29 +636,27 @@ "model = Mdl(phys_components, line_components, task_list)\n", "model.set_lindbladian(lindblad)\n", "model.set_dressed(dressed)" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.480926Z", + "iopub.status.busy": "2021-06-30T06:29:24.480595Z", + "iopub.status.idle": "2021-06-30T06:29:24.488368Z", + "shell.execute_reply": "2021-06-30T06:29:24.488635Z" + } + } }, { "cell_type": "markdown", - "id": "ff98ba0b", - "metadata": {}, "source": [ "### Generator" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 18, - "id": "f336afd4", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.492657Z", - "iopub.status.busy": "2021-06-30T06:29:24.492317Z", - "iopub.status.idle": "2021-06-30T06:29:24.494580Z", - "shell.execute_reply": "2021-06-30T06:29:24.494836Z" - } - }, - "outputs": [], "source": [ "generator = Gnr(\n", " devices={\n", @@ -733,29 +685,27 @@ " },\n", ")\n", "generator.devices[\"AWG\"].enable_drag_2()" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.492657Z", + "iopub.status.busy": "2021-06-30T06:29:24.492317Z", + "iopub.status.idle": "2021-06-30T06:29:24.494580Z", + "shell.execute_reply": "2021-06-30T06:29:24.494836Z" + } + } }, { "cell_type": "markdown", - "id": "97c70b13", - "metadata": {}, "source": [ "### Gateset" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 19, - "id": "4443c05a", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.501528Z", - "iopub.status.busy": "2021-06-30T06:29:24.501168Z", - "iopub.status.idle": "2021-06-30T06:29:24.511660Z", - "shell.execute_reply": "2021-06-30T06:29:24.511908Z" - } - }, - "outputs": [], "source": [ "gauss_params_single = {\n", " \"amp\": Qty(value=0.45, min_val=0.4, max_val=0.6, unit=\"V\"),\n", @@ -826,20 +776,35 @@ "ry90p.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(0.5 * np.pi)\n", "rx90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(np.pi)\n", "ry90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(1.5 * np.pi)" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.501528Z", + "iopub.status.busy": "2021-06-30T06:29:24.501168Z", + "iopub.status.idle": "2021-06-30T06:29:24.511660Z", + "shell.execute_reply": "2021-06-30T06:29:24.511908Z" + } + } }, { "cell_type": "markdown", - "id": "69e342d1", - "metadata": {}, "source": [ "### Experiment" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 20, - "id": "8d1770b4", + "source": [ + "parameter_map = PMap(\n", + " instructions=[QId, rx90p, ry90p, rx90m, ry90m], model=model, generator=generator\n", + ")\n", + "\n", + "exp = Exp(pmap=parameter_map)" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.514280Z", @@ -847,20 +812,16 @@ "iopub.status.idle": "2021-06-30T06:29:24.515429Z", "shell.execute_reply": "2021-06-30T06:29:24.515676Z" } - }, - "outputs": [], - "source": [ - "parameter_map = PMap(\n", - " instructions=[QId, rx90p, ry90p, rx90m, ry90m], model=model, generator=generator\n", - ")\n", - "\n", - "exp = Exp(pmap=parameter_map)" - ] + } }, { "cell_type": "code", "execution_count": 21, - "id": "0f1f677c", + "source": [ + "exp_opt_map = [[('Q1', 'anhar')], [('Q1', 'freq')]]\n", + "exp.pmap.set_opt_map(exp_opt_map)" + ], + "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.517795Z", @@ -868,34 +829,18 @@ "iopub.status.idle": "2021-06-30T06:29:24.518939Z", "shell.execute_reply": "2021-06-30T06:29:24.519217Z" } - }, - "outputs": [], - "source": [ - "exp_opt_map = [[('Q1', 'anhar')], [('Q1', 'freq')]]\n", - "exp.pmap.set_opt_map(exp_opt_map)" - ] + } }, { "cell_type": "markdown", - "id": "11e27c4c", - "metadata": {}, "source": [ "## Optimizer " - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 22, - "id": "a04fd2c7", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.522171Z", - "iopub.status.busy": "2021-06-30T06:29:24.521841Z", - "iopub.status.idle": "2021-06-30T06:29:24.523305Z", - "shell.execute_reply": "2021-06-30T06:29:24.523566Z" - } - }, - "outputs": [], "source": [ "datafiles = {\"orbit\": DATAFILE_PATH} # path to the dataset\n", "run_name = \"simple_model_learning\" # name of the optimization run\n", @@ -925,21 +870,20 @@ " ],\n", " ]\n", "} # the excited states of the qubit model, in this case it is 3-level" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.522171Z", + "iopub.status.busy": "2021-06-30T06:29:24.521841Z", + "iopub.status.idle": "2021-06-30T06:29:24.523305Z", + "shell.execute_reply": "2021-06-30T06:29:24.523566Z" + } + } }, { "cell_type": "code", "execution_count": 23, - "id": "cd302375", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.525945Z", - "iopub.status.busy": "2021-06-30T06:29:24.525624Z", - "iopub.status.idle": "2021-06-30T06:29:24.531653Z", - "shell.execute_reply": "2021-06-30T06:29:24.531905Z" - } - }, - "outputs": [], "source": [ "opt = ModelLearning(\n", " datafiles=datafiles,\n", @@ -954,40 +898,41 @@ ")\n", "\n", "opt.set_exp(exp)" - ] + ], + "outputs": [], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.525945Z", + "iopub.status.busy": "2021-06-30T06:29:24.525624Z", + "iopub.status.idle": "2021-06-30T06:29:24.531653Z", + "shell.execute_reply": "2021-06-30T06:29:24.531905Z" + } + } }, { "cell_type": "markdown", - "id": "7ccfa138", - "metadata": {}, "source": [ "## Model Learning" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "e7aee5cd", - "metadata": {}, "source": [ "We are now ready to learn from the data and improve our model" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 24, - "id": "84128d1c", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.534091Z", - "iopub.status.busy": "2021-06-30T06:29:24.533766Z", - "iopub.status.idle": "2021-06-30T06:33:52.900803Z", - "shell.execute_reply": "2021-06-30T06:33:52.900453Z" - } - }, + "source": [ + "opt.run()" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "C3:STATUS:Saving as: /home/users/anurag/c3/examples/ml_logs/simple_model_learning/2021_07_05_T_20_54_20/model_learn.log\n", "(6_w,12)-aCMA-ES (mu_w=3.7,w_1=40%) in dimension 2 (seed=612179, Mon Jul 5 20:54:20 2021)\n", @@ -1003,62 +948,59 @@ ] } ], - "source": [ - "opt.run()" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.534091Z", + "iopub.status.busy": "2021-06-30T06:29:24.533766Z", + "iopub.status.idle": "2021-06-30T06:33:52.900803Z", + "shell.execute_reply": "2021-06-30T06:33:52.900453Z" + } + } }, { "cell_type": "markdown", - "id": "515c7954", - "metadata": {}, "source": [ - "### Result of Model Learning" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "490cffd1", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:33:52.903299Z", - "iopub.status.busy": "2021-06-30T06:33:52.902952Z", - "iopub.status.idle": "2021-06-30T06:33:52.904734Z", - "shell.execute_reply": "2021-06-30T06:33:52.904981Z" - } - }, + "### Result of Model Learning" + ], + "metadata": {} + }, + { + "cell_type": "code", + "execution_count": 25, + "source": [ + "opt.current_best_goal" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "-0.031570491979296046" ] }, - "execution_count": 25, "metadata": {}, - "output_type": "execute_result" + "execution_count": 25 } ], - "source": [ - "opt.current_best_goal" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:33:52.903299Z", + "iopub.status.busy": "2021-06-30T06:33:52.902952Z", + "iopub.status.idle": "2021-06-30T06:33:52.904734Z", + "shell.execute_reply": "2021-06-30T06:33:52.904981Z" + } + } }, { "cell_type": "code", "execution_count": 26, - "id": "7ba610c1", - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:33:52.907388Z", - "iopub.status.busy": "2021-06-30T06:33:52.907044Z", - "iopub.status.idle": "2021-06-30T06:33:52.908879Z", - "shell.execute_reply": "2021-06-30T06:33:52.909149Z" - } - }, + "source": [ + "print(opt.pmap.str_parameters(opt.pmap.opt_map))" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "Q1-anhar : -210.057 MHz 2pi \n", "Q1-freq : 5.000 GHz 2pi \n", @@ -1066,83 +1008,79 @@ ] } ], - "source": [ - "print(opt.pmap.str_parameters(opt.pmap.opt_map))" - ] + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:33:52.907388Z", + "iopub.status.busy": "2021-06-30T06:33:52.907044Z", + "iopub.status.idle": "2021-06-30T06:33:52.908879Z", + "shell.execute_reply": "2021-06-30T06:33:52.909149Z" + } + } }, { "cell_type": "markdown", - "id": "738d2429", - "metadata": {}, "source": [ "## Visualisation & Analysis of Results" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "0c7fad79", - "metadata": {}, "source": [ "The Model Learning logs provide a useful way to visualise the learning process and also understand what's going wrong (or right). We now process these logs to read some data points and also plot some visualisations of the Model Learning process" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "2c13ef43", - "metadata": {}, "source": [ "### Open, Clean-up and Convert Logfiles" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 27, - "id": "046c4a0e", - "metadata": {}, - "outputs": [], "source": [ "LOGDIR = opt.logdir" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 28, - "id": "f2a2e284", - "metadata": {}, - "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"model_learn.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 29, - "id": "c68370b1", - "metadata": {}, + "source": [ + "params_names = [\n", + " item for sublist in (ast.literal_eval(log[3].strip(\"\\n\"))) for item in sublist\n", + "]\n", + "print(params_names)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "['Q1-anhar', 'Q1-freq']\n" ] } ], - "source": [ - "params_names = [\n", - " item for sublist in (ast.literal_eval(log[3].strip(\"\\n\"))) for item in sublist\n", - "]\n", - "print(params_names)" - ] + "metadata": {} }, { "cell_type": "code", "execution_count": 30, - "id": "15f7f01f", - "metadata": {}, - "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1152,33 +1090,35 @@ " temp_dict[param_name] = temp_dict[\"params\"][index]\n", " temp_dict.pop(\"params\")\n", " data_list_dict.append(temp_dict)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 31, - "id": "652908e8", - "metadata": {}, - "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "id": "14cd4fbc", - "metadata": {}, "source": [ "### Summary of Logs" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 32, - "id": "813a61d7", - "metadata": {}, + "source": [ + "data_df.describe()" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -1269,103 +1209,89 @@ "max 61.088377 -2.040499e+08 5.001544e+09" ] }, - "execution_count": 32, "metadata": {}, - "output_type": "execute_result" + "execution_count": 32 } ], - "source": [ - "data_df.describe()" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "11ce7936", - "metadata": {}, "source": [ "**Best Point**" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 33, - "id": "9538b6a2", - "metadata": {}, - "outputs": [], "source": [ "best_point_file = os.path.join(LOGDIR, 'best_point_model_learn.log')" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 34, - "id": "b1ece0ba", - "metadata": {}, + "source": [ + "with open(best_point_file, \"r\") as f:\n", + " best_point_log_dict = hjson.load(best_point_file)\n", + "\n", + "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n", + "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n", + "print(best_point_dict)" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "{'Q1-anhar': -210057285.86145476, 'Q1-freq': 5000081146.521889, 'goal': -0.031570491979296046}\n" ] } ], - "source": [ - "with open(best_point_file, \"r\") as f:\n", - " best_point = f.read()\n", - " best_point_log_dict = ast.literal_eval(best_point)\n", - "\n", - "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n", - "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n", - "print(best_point_dict)" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "0891a75d", - "metadata": {}, "source": [ "### Plotting" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "c4f0e7b2", - "metadata": {}, "source": [ "We use `matplotlib` to produce the plots below. Please make sure you have the same installed in your python environment." - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 35, - "id": "d830b2b5", - "metadata": {}, - "outputs": [], "source": [ "!pip install -q matplotlib" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 36, - "id": "dd160661", - "metadata": {}, - "outputs": [], "source": [ "from matplotlib.ticker import MaxNLocator\n", "from matplotlib import rcParams\n", "from matplotlib import cycler\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt " - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 37, - "id": "3f570687", - "metadata": {}, - "outputs": [], "source": [ "rcParams[\"axes.grid\"] = True\n", "rcParams[\"grid.linestyle\"] = \"--\"\n", @@ -1374,187 +1300,177 @@ "rcParams[\"text.usetex\"] = False\n", "rcParams[\"font.size\"] = 16\n", "rcParams[\"font.family\"] = \"serif\"" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "markdown", - "id": "120c1a64", - "metadata": {}, "source": [ "**In the plots below, the blue line shows the progress of the parameter optimization while the black and the red lines indicate the converged and true value respectively**" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "4edbd3c5", - "metadata": {}, "source": [ "### Qubit Anharmonicity" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 38, - "id": "2880e76e", - "metadata": {}, + "source": [ + "plot_item = \"Q1-anhar\"\n", + "true_value = -210e6\n", + "\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Iteration\")\n", + "ax.set_ylabel(plot_item)\n", + "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", + "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", + "ax.plot(data_df[plot_item])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, - "execution_count": 38, "metadata": {}, - "output_type": "execute_result" + "execution_count": 38 }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], - "source": [ - "plot_item = \"Q1-anhar\"\n", - "true_value = -210e6\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Iteration\")\n", - "ax.set_ylabel(plot_item)\n", - "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", - "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", - "ax.plot(data_df[plot_item])" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "99488cee", - "metadata": {}, "source": [ "### Qubit Frequency" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 39, - "id": "c8c50971", - "metadata": {}, + "source": [ + "plot_item = \"Q1-freq\"\n", + "true_value = 5e9\n", + "\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Iteration\")\n", + "ax.set_ylabel(plot_item)\n", + "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", + "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", + "ax.plot(data_df[plot_item])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, - "execution_count": 39, "metadata": {}, - "output_type": "execute_result" + "execution_count": 39 }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], - "source": [ - "plot_item = \"Q1-freq\"\n", - "true_value = 5e9\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Iteration\")\n", - "ax.set_ylabel(plot_item)\n", - "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", - "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", - "ax.plot(data_df[plot_item])" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "8de2b391", - "metadata": {}, "source": [ "### Goal Function" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 40, - "id": "0241a9f3", - "metadata": {}, + "source": [ + "plot_item = \"goal\"\n", + "\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Iteration\")\n", + "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", + "ax.set_ylabel(plot_item)\n", + "\n", + "ax.plot(data_df[plot_item])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, - "execution_count": 40, "metadata": {}, - "output_type": "execute_result" + "execution_count": 40 }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], - "source": [ - "plot_item = \"goal\"\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Iteration\")\n", - "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", - "ax.set_ylabel(plot_item)\n", - "\n", - "ax.plot(data_df[plot_item])" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "14bcd662", - "metadata": {}, "source": [ "# Sensitivity Analysis" - ] + ], + "metadata": {} }, { "cell_type": "markdown", - "id": "bede11aa", - "metadata": {}, "source": [ "Another interesting study to understand if our dataset is indeed helpful in improving certain model parameters is to perform a Sensitivity Analysis. The purpose of this exercise is to scan the Model Parameters of interest (eg, qubit frequency or anharmonicity) across a range of values and notice a prominent dip in the Model Learning Goal Function around the best-fit values" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 41, - "id": "772c0a9b", - "metadata": {}, - "outputs": [], "source": [ "run_name = \"Sensitivity\"\n", "dir_path = \"sensi_logs\"\n", @@ -1564,14 +1480,13 @@ " [-215e6, -205e6],\n", " [4.9985e9, 5.0015e9],\n", "]" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 42, - "id": "b6fda647", - "metadata": {}, - "outputs": [], "source": [ "sense_opt = Sensitivity(\n", " datafiles=datafiles,\n", @@ -1588,17 +1503,20 @@ ")\n", "\n", "sense_opt.set_exp(exp)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 43, - "id": "dd41e6df", - "metadata": {}, + "source": [ + "sense_opt.run()" + ], "outputs": [ { - "name": "stdout", "output_type": "stream", + "name": "stdout", "text": [ "C3:STATUS:Sweeping [['Q1-anhar']]: [-215000000.0, -205000000.0]\n", "C3:STATUS:Saving as: /home/users/anurag/c3/examples/sensi_logs/Sensitivity/2021_07_05_T_20_56_46/sensitivity.log\n", @@ -1607,46 +1525,38 @@ ] } ], - "source": [ - "sense_opt.run()" - ] + "metadata": {} }, { "cell_type": "markdown", - "id": "bc381eb3", - "metadata": {}, "source": [ "## Anharmonicity" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 44, - "id": "ba556d1a", - "metadata": {}, - "outputs": [], "source": [ "LOGDIR = sense_opt.logdir_list[0]" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 45, - "id": "65a92f5a", - "metadata": {}, - "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 46, - "id": "c40e969a", - "metadata": {}, - "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1654,94 +1564,88 @@ " temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n", " param = temp_dict[\"params\"][0]\n", " data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 47, - "id": "dea53bc5", - "metadata": {}, - "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 48, - "id": "42e4f5eb", - "metadata": { - "scrolled": true - }, + "source": [ + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Q1-Anharmonicity [Hz]\")\n", + "ax.set_ylabel(\"Goal Function\")\n", + "ax.axvline(x=best_point_dict[\"Q1-anhar\"], color=\"black\", linestyle=\"-.\")\n", + "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "" ] }, - "execution_count": 48, "metadata": {}, - "output_type": "execute_result" + "execution_count": 48 }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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", 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" ] }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], - "source": [ - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Q1-Anharmonicity [Hz]\")\n", - "ax.set_ylabel(\"Goal Function\")\n", - "ax.axvline(x=best_point_dict[\"Q1-anhar\"], color=\"black\", linestyle=\"-.\")\n", - "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" - ] + "metadata": { + "scrolled": true + } }, { "cell_type": "markdown", - "id": "a1517074", - "metadata": {}, "source": [ "## Frequency" - ] + ], + "metadata": {} }, { "cell_type": "code", "execution_count": 49, - "id": "d067e561", - "metadata": {}, - "outputs": [], "source": [ "LOGDIR = sense_opt.logdir_list[1]" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 50, - "id": "a32f36f9", - "metadata": {}, - "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 51, - "id": "0461adb3", - "metadata": {}, - "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1749,55 +1653,55 @@ " temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n", " param = temp_dict[\"params\"][0]\n", " data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 52, - "id": "9d1eb7fb", - "metadata": {}, - "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ] + ], + "outputs": [], + "metadata": {} }, { "cell_type": "code", "execution_count": 53, - "id": "d470a4f1", - "metadata": {}, + "source": [ + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Q1-Frequency [Hz]\")\n", + "ax.set_ylabel(\"Goal Function\")\n", + "ax.axvline(x=best_point_dict[\"Q1-freq\"], color=\"black\", linestyle=\"-.\")\n", + "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" + ], "outputs": [ { + "output_type": "execute_result", "data": { "text/plain": [ "" ] }, - "execution_count": 53, "metadata": {}, - "output_type": "execute_result" + "execution_count": 53 }, { + "output_type": "display_data", "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" - }, - "output_type": "display_data" + } } ], - "source": [ - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Q1-Frequency [Hz]\")\n", - "ax.set_ylabel(\"Goal Function\")\n", - "ax.axvline(x=best_point_dict[\"Q1-freq\"], color=\"black\", linestyle=\"-.\")\n", - "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" - ] + "metadata": {} } ], "metadata": { @@ -1824,4 +1728,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} +} \ No newline at end of file From ddce04d2b2a50b64fbb60b45ec8c6e0fda7643cd Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 8 Sep 2021 16:20:53 +0200 Subject: [PATCH 16/80] Fixed typo --- examples/Simulated_Model_Learning.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/Simulated_Model_Learning.ipynb b/examples/Simulated_Model_Learning.ipynb index 49398dd1..479a8d28 100644 --- a/examples/Simulated_Model_Learning.ipynb +++ b/examples/Simulated_Model_Learning.ipynb @@ -1236,7 +1236,7 @@ "execution_count": 34, "source": [ "with open(best_point_file, \"r\") as f:\n", - " best_point_log_dict = hjson.load(best_point_file)\n", + " best_point_log_dict = hjson.load(f)\n", "\n", "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n", "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n", From 72b7621a9aef1c526de9e9cb5cbf382057bb7f8a Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Thu, 9 Sep 2021 10:04:15 +0200 Subject: [PATCH 17/80] Simplified generation of table --- c3/utils/log_reader.py | 55 ++++++++++++++++++------------------------ 1 file changed, 24 insertions(+), 31 deletions(-) diff --git a/c3/utils/log_reader.py b/c3/utils/log_reader.py index e58fd6a6..1e45bb1e 100755 --- a/c3/utils/log_reader.py +++ b/c3/utils/log_reader.py @@ -20,38 +20,31 @@ def show_table(log: Dict[str, Any], console: Console) -> None: console : Console Rich console for output. """ - if log: - opt_map = log["opt_map"] - optim_status = log["optim_status"] - units = log["units"] - params = optim_status["params"] - grads = optim_status.pop("gradient", None) + opt_map = log["opt_map"] + optim_status = log["optim_status"] + units = log["units"] + params = optim_status["params"] + if "gradient" not in optim_status: + grads = [0] * len(params) + else: + grads = optim_status["gradient"] + table = Table(show_header=True, header_style="bold magenta") + table.add_column("Parameter") + table.add_column("Value", justify="right") + table.add_column("Gradient", justify="right") + for ii, equiv_ids in enumerate(opt_map): + par = params[ii] + par = num3str(par) + par_id = equiv_ids[0] + table.add_row(par_id, par + units[ii], num3str(grads[ii]) + units[ii]) + for par_id in equiv_ids[1:]: + table.add_row(par_id, "''", "''") - table = Table(show_header=True, header_style="bold magenta") - table.add_column("Parameter") - table.add_column("Value", justify="right") - if grads is not None: - table.add_column("Gradient", justify="right") - for ii, equiv_ids in enumerate(opt_map): - par = params[ii] - par = num3str(par) - par_id = equiv_ids[0] - if grads is not None: - table.add_row(par_id, par + units[ii], num3str(grads[ii]) + units[ii]) - if len(equiv_ids) > 1: - for par_id in equiv_ids[1:]: - table.add_row(par_id, "''", "''") - else: - table.add_row(par_id, par + units[ii]) - if len(equiv_ids) > 1: - for par_id in equiv_ids[1:]: - table.add_row(par_id, "''") - - console.clear() - print( - f"Optimization reached {optim_status['goal']:0.3g} at {optim_status['time']}\n" - ) - console.print(table) + console.clear() + print( + f"Optimization reached {optim_status['goal']:0.3g} at {optim_status['time']}\n" + ) + console.print(table) if __name__ == "__main__": From 9b66803c2e546554cdb29264f017b73fa976d3ad Mon Sep 17 00:00:00 2001 From: frosati1 Date: Fri, 22 Oct 2021 12:13:19 +0200 Subject: [PATCH 18/80] Added RK4 and trotterized methods --- c3/libraries/propagation.py | 83 +++++++++++++++++++++++++++++++++++-- 1 file changed, 79 insertions(+), 4 deletions(-) diff --git a/c3/libraries/propagation.py b/c3/libraries/propagation.py index 8d6c75ea..664c7f6f 100644 --- a/c3/libraries/propagation.py +++ b/c3/libraries/propagation.py @@ -9,6 +9,63 @@ ) +def step_vonNeumann_psi(psi, h, dt): + return -1j * dt * tf.linalg.matvec(h, psi) + + +def gen_dUs_RK4(h0, hks, cflds_t, dt): + U = [] + tot_dim = tf.shape(h0) + dim = tot_dim[1] + temp = [] + if hks is not None: + h = h0 + ii = 0 + while ii < len(hks): + h += cflds_t[ii] * hks[ii] + ii += 1 + + else: + h = h0 + + for jj in range(0, len(h) - 2, 2): + dU = [] + for ii in range(dim): + psi = tf.one_hot(ii, dim, dtype=tf.complex128) + + k1 = step_vonNeumann_psi(psi, h[jj], dt) + k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[jj + 1], dt) + k3 = step_vonNeumann_psi(psi + k2 / 2.0, h[jj + 1], dt) + k4 = step_vonNeumann_psi(psi + k3, h[jj + 2], dt) + + psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 + dU.append(psi) + temp = tf.stack(dU) + U.append(temp) + return U + + +def gen_U_RK4(h, dt): + + dU = [] + tot_dim = tf.shape(h) + dim = tot_dim[1] + for ii in range(dim): + psi = tf.one_hot(ii, dim, dtype=tf.complex128) + + for jj in range(len(h), 2): + + k1 = step_vonNeumann_psi(psi, h[jj], dt) + k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[jj + 1], dt) + k3 = step_vonNeumann_psi(psi + k2 / 2.0, h[jj + 1], dt) + k4 = step_vonNeumann_psi(psi + k3, h[jj + 2], dt) + + psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 + dU.append(psi) + dU = tf.stack(dU) + return tf.transpose(dU) + + @tf.function def tf_dU_of_t(h0, hks, cflds_t, dt): """ @@ -39,7 +96,7 @@ def tf_dU_of_t(h0, hks, cflds_t, dt): # terms = int(1e12 * dt) + 2 # dU = tf_expm(-1j * h * dt, terms) # TODO Make an option for the exponentation method - dU = tf.linalg.expm(-1j * h * dt) + dU = gen_U_RK4(h, dt) # tf.linalg.expm(-1j * h * dt) return dU @@ -106,6 +163,23 @@ def tf_propagation_vectorized(h0, hks, cflds_t, dt): return tf.linalg.expm(dh) +def pwc_trott_drift(h0, hks, cflds_t, dt): + dt = tf.cast(dt, dtype=tf.complex128) + cflds_t = tf.cast(cflds_t, dtype=tf.complex128) + hks = tf.cast(hks, dtype=tf.complex128) + e, v = tf.linalg.eigh(h0) + ort = tf.cast(v, dtype=tf.complex128) + dE = tf.math.exp(-1.0j * tf.math.real(e) * dt) + dU0 = ort @ tf.linalg.diag(dE) @ ort.T + prod = cflds_t * hks + ht = tf.reduce_sum(prod, axis=0) + comm = h0 @ ht - ht @ h0 + dh = -1.0j * ht * dt + dcomm = -comm * dt ** 2 / 2.0 + dUs = dU0 @ tf.linalg.expm(dh) @ (tf.identity(dU0) - dcomm) + return dUs + + def tf_batch_propagate(hamiltonian, hks, signals, dt, batch_size): """ Propagate signal in batches @@ -135,6 +209,7 @@ def tf_batch_propagate(hamiltonian, hks, signals, dt, batch_size): batch_array = batch_array.write( i, signals[i * batch_size : i * batch_size + batch_size] ) + else: batches = int(tf.math.ceil(hamiltonian.shape[0] / batch_size)) batch_array = tf.TensorArray( @@ -149,9 +224,9 @@ def tf_batch_propagate(hamiltonian, hks, signals, dt, batch_size): for i in range(batches): x = batch_array.read(i) if signals is not None: - result = tf_propagation_vectorized(hamiltonian, hks, x, dt) + result = tf_propagation(hamiltonian, hks, x, dt) else: - result = tf_propagation_vectorized(x, None, None, dt) + result = tf_propagation(x, None, None, dt) dUs_array = dUs_array.write(i, result) return dUs_array.concat() @@ -184,7 +259,7 @@ def tf_propagation(h0, hks, cflds, dt): cf_t = [] for fields in cflds: cf_t.append(tf.cast(fields[ii], tf.complex128)) - dUs.append(tf_dU_of_t(h0, hks, cf_t, dt)) + dUs.append(tf_dU_of_t(h0, hks, cf_t, dt)) # return dUs From 24a11bc441d8a22c06bd9993fcdf19dbbb5b64c6 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Fri, 22 Oct 2021 13:40:45 +0200 Subject: [PATCH 19/80] added propagation method in cfg --- c3/main.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/c3/main.py b/c3/main.py index 51624585..1297c001 100755 --- a/c3/main.py +++ b/c3/main.py @@ -50,6 +50,7 @@ def run_cfg(cfg, opt_config_filename, debug=False): model = None gen = None exp = None + prop_meth = cfg.pop("propagation_method", None) if "model" in cfg: model = Model() model.read_config(cfg.pop("model")) @@ -59,13 +60,13 @@ def run_cfg(cfg, opt_config_filename, debug=False): if "instructions" in cfg: pmap = ParameterMap(model=model, generator=gen) pmap.read_config(cfg.pop("instructions")) - exp = Experiment(pmap) + exp = Experiment(pmap, prop_method=prop_meth) if "exp_cfg" in cfg: - exp = Experiment() + exp = Experiment(prop_method=prop_meth) exp.read_config(cfg.pop("exp_cfg")) if exp is None: print("C3:STATUS: No instructions specified. Performing quick setup.") - exp = Experiment() + exp = Experiment(prop_method=prop_meth) exp.quick_setup(cfg) exp.set_opt_gates(cfg.pop("opt_gates", None)) From d9fe309e6d8cb18aa90d9323e3059e22f0bfae82 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Fri, 22 Oct 2021 13:42:41 +0200 Subject: [PATCH 20/80] option for prop method and moved propagation to library --- c3/experiment.py | 184 ++++++++++++++++++----------------------------- 1 file changed, 69 insertions(+), 115 deletions(-) diff --git a/c3/experiment.py b/c3/experiment.py index 1ec58a7a..f7ef95af 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -15,7 +15,7 @@ import hjson import numpy as np import tensorflow as tf -from typing import Dict +from typing import Dict, List import time from c3.c3objs import hjson_encode, hjson_decode @@ -24,16 +24,13 @@ from c3.signal.gates import Instruction from c3.model import Model from c3.utils.tf_utils import ( - tf_matmul_left, tf_state_to_dm, tf_super, tf_vec_to_dm, ) -from c3.libraries.propagation import ( - tf_batch_propagate, - tf_propagation_lind, -) +from c3.libraries.propagation import unitary_provider, state_provider + from c3.utils.qt_utils import perfect_single_q_parametric_gate @@ -57,19 +54,35 @@ class Experiment: """ - def __init__(self, pmap: ParameterMap = None): + def __init__(self, pmap: ParameterMap = None, prop_method = None): self.pmap = pmap self.opt_gates = None self.propagators: Dict[str, tf.Tensor] = {} - self.partial_propagators: dict = {} + self.partial_propagators: Dict = {} self.created_by = None - self.logdir: str = None + self.logdir: str = "" self.propagate_batch_size = None self.use_control_fields = True self.overwrite_propagators = True # Keep only currently computed propagators self.compute_propagators_timestamp = 0 self.stop_partial_propagator_gradient = True self.evaluate = self.evaluate_legacy + self.set_prop_method(prop_method) + + def set_prop_method(self, prop_method=None) -> None: + """ + Configure the selected propagation method by either linking the function handle or + looking it up in the library. + """ + if prop_method is None: + self.propagation = unitary_provider["pwc"] + elif isinstance(prop_method, str): + try: + self.propagation = unitary_provider[prop_method] + except KeyError: + self.propagation = state_provider[prop_method] + elif callable(prop_method): + self.propagation = prop_method def enable_qasm(self) -> None: """ @@ -180,7 +193,7 @@ def read_config(self, filepath: str) -> None: cfg = hjson.loads(cfg_file.read(), object_pairs_hook=hjson_decode) self.from_dict(cfg) - def from_dict(self, cfg: dict) -> None: + def from_dict(self, cfg: Dict) -> None: """ Load experiment from dictionary """ @@ -202,11 +215,11 @@ def write_config(self, filepath: str) -> None: with open(filepath, "w") as cfg_file: hjson.dump(self.asdict(), cfg_file, default=hjson_encode) - def asdict(self) -> dict: + def asdict(self) -> Dict: """ Return a dictionary compatible with config files. """ - exp_dict: Dict[str, dict] = {} + exp_dict: Dict[str, Dict] = {} exp_dict["instructions"] = {} for name, instr in self.pmap.instructions.items(): exp_dict["instructions"][name] = instr.asdict() @@ -390,6 +403,38 @@ def get_perfect_gates(self, gate_keys: list = None) -> Dict[str, np.array]: return gates + def compute_states(self) -> Dict[Instruction, List[tf.Tensor]]: + """Employ a state solver to compute the trajectory of the system. + + Returns + ------- + List[tf.tensor] + List of states of the system from simulation. + + """ + model = self.pmap.model + generator = self.pmap.generator + instructions = self.pmap.instructions + states = {} + + gate_ids = self.opt_gates + if gate_ids is None: + gate_ids = instructions.keys() + + for gate in gate_ids: + try: + instr = instructions[gate] + except KeyError: + raise Exception( + f"C3:Error: Gate '{gate}' is not defined." + f" Available gates are:\n {list(instructions.keys())}." + ) + signal = generator.generate_signals(instr) + result = self..pyagation(model, signal) + states[instr] = result["states"] + self.states = states + return result + def compute_propagators(self): """ Compute the unitary representation of operations. If no operations are @@ -403,7 +448,8 @@ def compute_propagators(self): model = self.pmap.model generator = self.pmap.generator instructions = self.pmap.instructions - gates = {} + propagators = {} + partial_propagators = {} gate_ids = self.opt_gates if gate_ids is None: gate_ids = instructions.keys() @@ -416,8 +462,9 @@ def compute_propagators(self): f"C3:Error: Gate '{gate}' is not defined." f" Available gates are:\n {list(instructions.keys())}." ) - signal = generator.generate_signals(instr) - U = self.propagation(signal, gate) + result = self.propagation(model, generator, instr) + U = result["U"] + dUs = result["dUs"] if model.use_FR: # TODO change LO freq to at the level of a line freqs = {} @@ -459,111 +506,18 @@ def compute_propagators(self): ) dephasing_channel = model.get_dephasing_channel(t_final, amps) U = tf.matmul(dephasing_channel, U) - gates[gate] = U + propagators[gate] = U + partial_propagators[gate] = dUs # TODO we might want to move storing of the propagators to the instruction object if self.overwrite_propagators: - self.propagators = gates + self.propagators = propagators + self.partial_propagators = partial_propagators else: - self.propagators.update(gates) + self.propagators.update(propagators) + self.partial_propagators.update(partial_propagators) self.compute_propagators_timestamp = time.time() - return gates - - def propagation(self, signal: dict, gate): - """ - Solve the equation of motion (Lindblad or Schrödinger) for a given control - signal and Hamiltonians. - - Parameters - ---------- - signal: dict - Waveform of the control signal per drive line. - gate: str - Identifier for one of the gates. - - Returns - ------- - unitary - Matrix representation of the gate. - """ - model = self.pmap.model - - if self.use_control_fields: - hamiltonian, hctrls = model.get_Hamiltonians() - signals = [] - hks = [] - for key in signal: - signals.append(signal[key]["values"]) - ts = signal[key]["ts"] - hks.append(hctrls[key]) - signals = tf.cast(signals, tf.complex128) - hks = tf.cast(hks, tf.complex128) - else: - hamiltonian = model.get_Hamiltonian(signal) - ts_list = [sig["ts"][1:] for sig in signal.values()] - ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) - signals = None - hks = None - assert np.all( - tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0]) - ) - assert np.all( - tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) - ) - - # TODO: is this compatible with lindbladian - if model.max_excitations: - cutter = model.ex_cutter - hamiltonian = cutter @ hamiltonian @ cutter.T - if hks is not None: - cutter_tf = tf.cast(cutter, tf.complex128) - hks = tf.matmul(cutter_tf, tf.matmul(hks, cutter_tf, transpose_b=True)) - - dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) - - if model.lindbladian: - col_ops = model.get_Lindbladians() - if model.max_excitations: - cutter = model.ex_cutter - col_ops = [cutter @ col_op @ cutter.T for col_op in col_ops] - dUs = tf_propagation_lind(hamiltonian, hks, col_ops, signals, dt) - else: - batch_size = ( - self.propagate_batch_size - if self.propagate_batch_size - else len(hamiltonian) - ) - batch_size = ( - len(hamiltonian) if batch_size > len(hamiltonian) else batch_size - ) - batch_size = tf.constant(batch_size, tf.int32) - dUs = tf_batch_propagate( - hamiltonian, hks, signals, dt, batch_size=batch_size - ) - - U = tf_matmul_left(tf.cast(dUs, tf.complex128)) - if model.max_excitations: - U = cutter.T @ U @ cutter - ex_cutter = tf.cast(tf.expand_dims(model.ex_cutter, 0), tf.complex128) - if self.stop_partial_propagator_gradient: - self.partial_propagators[gate] = tf.stop_gradient( - tf.linalg.matmul( - tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), - ex_cutter, - ) - ) - else: - self.partial_propagators[gate] = tf.stop_gradient( - tf.linalg.matmul( - tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), - ex_cutter, - ) - ) - else: - self.partial_propagators[gate] = dUs - - self.ts = ts - return U + return propagators def set_opt_gates(self, gates): """ From 481316bc2f74b84ec859928800eac617be5cfa41 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Mon, 25 Oct 2021 17:57:44 +0200 Subject: [PATCH 21/80] Revert edit to changelog Please only update the changelog by adding new sections and not removing any of the instructions at the top This reverts commit 863469e68dedbd02f7c2d24e139ce46a4955d18a. --- CHANGELOG.md | 2 -- 1 file changed, 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index cd6465a4..6526f6a9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,8 +9,6 @@ This Changelog tracks all past changes to this project as well as details about - `fixed` for any bug fixes. - `security` in case of vulnerabilities. -- `changed` The generator now can handle any list of devices that forms a directed graph #129 - ## Version `1.3` - 20 Jul 2021 ### Summary From 388a9af0032f74b8e74e7ec07fb5b0ba4a90f71d Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Tue, 26 Oct 2021 10:44:57 +0200 Subject: [PATCH 22/80] fix handling of anharmonicity in transmon class --- CHANGELOG.md | 6 ++++++ c3/libraries/chip.py | 3 +-- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6526f6a9..2f52862a 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,12 @@ This Changelog tracks all past changes to this project as well as details about - `fixed` for any bug fixes. - `security` in case of vulnerabilities. +## Upcoming Version + +### Details + +- `fixed` handling of anharmonicity in transmons with two levels #145 + ## Version `1.3` - 20 Jul 2021 ### Summary diff --git a/c3/libraries/chip.py b/c3/libraries/chip.py index 703dd33e..755e20eb 100755 --- a/c3/libraries/chip.py +++ b/c3/libraries/chip.py @@ -408,7 +408,6 @@ def get_Hamiltonian( ): Hs = self.get_transformed_hamiltonians(transform) H_freq = Hs["freq"] - H_anhar = Hs["anhar"] if isinstance(signal, dict): sig = signal["values"] @@ -420,7 +419,7 @@ def get_Hamiltonian( h = freq * H_freq if self.hilbert_dim > 2: - h += self.get_anhar() * H_anhar + h += self.get_anhar() * Hs["anhar"] return h def get_Lindbladian(self, dims): From 1cb535c5250a1e354cd996f96ce4bea9c3fc3228 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Tue, 26 Oct 2021 10:56:15 +0200 Subject: [PATCH 23/80] fix changelog --- CHANGELOG.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6526f6a9..bac93cb5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,6 +9,12 @@ This Changelog tracks all past changes to this project as well as details about - `fixed` for any bug fixes. - `security` in case of vulnerabilities. +## Upcoming Version + +### Details + +- `changed` The generator now can handle any list of devices that forms a directed graph #129 + ## Version `1.3` - 20 Jul 2021 ### Summary From 795c8f5a3e95c60e16af2bc338f8f3dacb4c3a67 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 9 Nov 2021 16:57:36 +0100 Subject: [PATCH 24/80] Added write_values method. --- c3/parametermap.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/c3/parametermap.py b/c3/parametermap.py index 7ebe8089..52394a11 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -81,6 +81,26 @@ def load_values(self, init_point): init_p = best["optim_status"]["params"] self.set_parameters(init_p, best_opt_map) + def write_values(self, path: str) -> None: + """ + Write current parameter values to file. + + Parameters + ---------- + path : str + Location of the resulting logfile. + """ + with open(path, "w") as value_file: + val_dict = { + "opt_map": self.get_opt_map(), + "units": self.get_opt_units(), + "optim_status": { + "params": [par.numpy().tolist() for par in self.get_parameters()] + }, + } + value_file.write(hjson.dumps(val_dict, default=hjson_encode)) + value_file.write("\n") + def read_config(self, filepath: str) -> None: """ Load a file and parse it to create a ParameterMap object. From 39d6c6b41814897ab458c6389024235169c1dd9c Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 9 Nov 2021 17:12:20 +0100 Subject: [PATCH 25/80] Optimizer uses new method. --- c3/optimizers/optimizer.py | 12 ++++-------- c3/parametermap.py | 10 ++++++---- 2 files changed, 10 insertions(+), 12 deletions(-) diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 2146cb23..158e016f 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -171,14 +171,10 @@ def log_parameters(self) -> None: if self.optim_status["goal"] < self.current_best_goal: self.current_best_goal = self.optim_status["goal"] self.current_best_params = self.optim_status["params"] - with open(self.logdir + "best_point_" + self.logname, "w") as best_point: - best_dict = { - "opt_map": self.pmap.get_opt_map(), - "units": self.pmap.get_opt_units(), - "optim_status": self.optim_status, - } - best_point.write(hjson.dumpsJSON(best_dict, default=hjson_encode)) - best_point.write("\n") + self.pmap.write_values( + path=self.logdir + "best_point_" + self.logname, + optim_status=self.optim_status, + ) if self.store_unitaries: self.exp.store_Udict(self.optim_status["goal"]) self.exp.store_unitaries_counter += 1 diff --git a/c3/parametermap.py b/c3/parametermap.py index 52394a11..8ad87e15 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -81,7 +81,7 @@ def load_values(self, init_point): init_p = best["optim_status"]["params"] self.set_parameters(init_p, best_opt_map) - def write_values(self, path: str) -> None: + def write_values(self, path: str, optim_status=None) -> None: """ Write current parameter values to file. @@ -90,13 +90,15 @@ def write_values(self, path: str) -> None: path : str Location of the resulting logfile. """ + if optim_status is None: + optim_status = ( + {"params": [par.numpy().tolist() for par in self.get_parameters()]}, + ) with open(path, "w") as value_file: val_dict = { "opt_map": self.get_opt_map(), "units": self.get_opt_units(), - "optim_status": { - "params": [par.numpy().tolist() for par in self.get_parameters()] - }, + "optim_status": optim_status, } value_file.write(hjson.dumps(val_dict, default=hjson_encode)) value_file.write("\n") From e768c7aebf0b9efd534b29bdb68439c52b9e222c Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 9 Nov 2021 17:16:38 +0100 Subject: [PATCH 26/80] Fixed some typing. --- c3/optimizers/optimizer.py | 10 +++++----- c3/parametermap.py | 2 +- test/test_parameter_map.py | 2 +- 3 files changed, 7 insertions(+), 7 deletions(-) diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 158e016f..56df2059 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -2,7 +2,7 @@ import os import time -from typing import Callable, Union, List, Dict, Any +from typing import Callable, Union, List, Dict, Optional, Any import numpy as np import tensorflow as tf import hjson @@ -44,16 +44,16 @@ def __init__( self.evaluation = 0 self.store_unitaries = store_unitaries self.created_by = None - self.logname: str = None + self.logname: str = "" self.options = None - self.__dir_path: str = None - self.logdir: str = None + self.__dir_path: str = "" + self.logdir: str = "" self.set_algorithm(algorithm) self.logger = [] if logger is not None: self.logger = logger - def set_algorithm(self, algorithm: Callable) -> None: + def set_algorithm(self, algorithm: Optional[Callable]) -> None: if algorithm: self.algorithm = algorithm else: diff --git a/c3/parametermap.py b/c3/parametermap.py index 8ad87e15..bb709431 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -386,7 +386,7 @@ def get_key_from_scaled_index(self, idx, opt_map=None) -> str: curr_indx += par_len if idx < curr_indx: return key - return None + return "" def set_opt_map(self, opt_map) -> None: """ diff --git a/test/test_parameter_map.py b/test/test_parameter_map.py index dbe3b961..422b27ca 100644 --- a/test/test_parameter_map.py +++ b/test/test_parameter_map.py @@ -232,7 +232,7 @@ def test_get_key_from_scaled_index(): assert pmap.get_key_from_scaled_index(1) == "rx90p[0]-d1-gauss-delta" assert pmap.get_key_from_scaled_index(2) == "rx90p[0]-d1-gauss-freq_offset" assert pmap.get_key_from_scaled_index(3) == "id[0]-d1-carrier-framechange" - assert pmap.get_key_from_scaled_index(4) is None + assert pmap.get_key_from_scaled_index(4) == "" @pytest.mark.unit From 06b3c965e6252720f356726049b5ede541de6f23 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 9 Nov 2021 17:54:21 +0100 Subject: [PATCH 27/80] Removed accidental tuple. --- c3/parametermap.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/c3/parametermap.py b/c3/parametermap.py index bb709431..d6b65d4a 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -91,9 +91,9 @@ def write_values(self, path: str, optim_status=None) -> None: Location of the resulting logfile. """ if optim_status is None: - optim_status = ( - {"params": [par.numpy().tolist() for par in self.get_parameters()]}, - ) + optim_status = { + "params": [par.numpy().tolist() for par in self.get_parameters()] + } with open(path, "w") as value_file: val_dict = { "opt_map": self.get_opt_map(), From 2078a65946cfd98d228bfed7286858d065a16c16 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:04:22 +0100 Subject: [PATCH 28/80] max_excitations method for get_Ham and propagation included --- c3/model.py | 88 ++++++++++++++++++++++++++++++++++++++--------------- 1 file changed, 63 insertions(+), 25 deletions(-) diff --git a/c3/model.py b/c3/model.py index 73e89f3f..4c3ea44c 100755 --- a/c3/model.py +++ b/c3/model.py @@ -57,6 +57,7 @@ def __init__(self, subsystems=None, couplings=None, tasks=None, max_excitations= self.set_components(subsystems, couplings, max_excitations) if tasks: self.set_tasks(tasks) + self.controllability = True def get_ground_state(self) -> tf.constant: gs = [[0] * self.tot_dim] @@ -166,11 +167,19 @@ def set_max_excitations(self, max_excitations) -> None: proj.append(line) ii += 1 excitation_cutter = np.array(proj) - self.ex_cutter = excitation_cutter - else: - self.ex_cutter = np.eye(self.tot_dim) + self.ex_cutter = tf.convert_to_tensor( + excitation_cutter, dtype=tf.complex128 + ) self.max_excitations = max_excitations + def cut_excitations(self, op): + cutter = self.ex_cutter + return cutter @ op @ tf.transpose(cutter) + + def blowup_excitations(self, op): + cutter = self.ex_cutter + return tf.transpose(cutter) @ op @ cutter + def read_config(self, filepath: str) -> None: """ Load a file and parse it to create a Model object. @@ -298,40 +307,69 @@ def list_parameters(self): return ids def get_Hamiltonians(self): + drift = [] + controls = [] + if self.dressed: - return self.dressed_drift_ham, self.dressed_control_hams + drift = self.dressed_drift_ham + controls = self.dressed_control_hams else: - return self.drift_ham, self.control_hams + drift = self.drift_ham + controls = self.control_hams + if self.max_excitations: + drift = self.cut_excitations(drift) + controls = self.cut_excitations(controls) + return drift, controls + + def get_sparse_Hamiltonians(self): + drift, controls = self.get_Hamiltonians + sparse_drift = self.blowup_excitations(drift) + sparse_controls = tf.vectorized_map(self.blowup_excitations, controls) + return sparse_drift, sparse_controls def get_Hamiltonian(self, signal=None): """Get a hamiltonian with an optional signal. This will return an hamiltonian over time. - Can be used e.g. for tuning the frequency of a transmon, where the control hamiltonian is not easily accessible""" + Can be used e.g. for tuning the frequency of a transmon, where the control hamiltonian is not easily accessible. + If max.excitation is non-zero the resulting Hamiltonian is cut accordingly""" if signal is None: if self.dressed: - return self.dressed_drift_ham + signal_hamiltonian = self.dressed_drift_ham else: - return self.drift_ham - if self.dressed: - hamiltonians = copy.deepcopy(self.__dressed_hamiltonians) - transform = self.transform + signal_hamiltonian = self.drift_ham else: - hamiltonians = copy.deepcopy(self.__hamiltonians) - transform = None - for key, sig in signal.items(): - if key in self.subsystems: - hamiltonians[key] = self.subsystems[key].get_Hamiltonian(sig, transform) - elif key in self.couplings: - hamiltonians[key] = self.couplings[key].get_Hamiltonian(sig, transform) + if self.dressed: + hamiltonians = copy.deepcopy(self.__dressed_hamiltonians) + transform = self.transform else: - raise Exception(f"Signal channel {key} not in model systems") - signal_hamiltonian = sum( - [ - tf.expand_dims(h, 0) if len(h.shape) == 2 else h - for h in hamiltonians.values() - ] - ) + hamiltonians = copy.deepcopy(self.__hamiltonians) + transform = None + for key, sig in signal.items(): + if key in self.subsystems: + hamiltonians[key] = self.subsystems[key].get_Hamiltonian( + sig, transform + ) + elif key in self.couplings: + hamiltonians[key] = self.couplings[key].get_Hamiltonian( + sig, transform + ) + else: + raise Exception(f"Signal channel {key} not in model systems") + + signal_hamiltonian = sum( + [ + tf.expand_dims(h, 0) if len(h.shape) == 2 else h + for h in hamiltonians.values() + ] + ) + + if self.max_excitations: + signal_hamiltonian = self.cut_excitations(signal_hamiltonian) + return signal_hamiltonian + def get_sparse_Hamiltonian(self, signal=None): + return self.blowup_excitations(self.get_Hamiltonian(signal)) + def get_Lindbladians(self): if self.dressed: return self.dressed_col_ops From 6b6fe2d9d2f53554786b83067b326fb6300b0422 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:13:47 +0100 Subject: [PATCH 29/80] added exp.prop and rk4 --- c3/libraries/propagation.py | 239 +++++++++++++++++++++++++++++++++--- 1 file changed, 223 insertions(+), 16 deletions(-) diff --git a/c3/libraries/propagation.py b/c3/libraries/propagation.py index 664c7f6f..867de8e1 100644 --- a/c3/libraries/propagation.py +++ b/c3/libraries/propagation.py @@ -1,6 +1,10 @@ "A library for propagators and closely related functions" - +import numpy as np import tensorflow as tf +from typing import Dict +from c3.model import Model +from c3.generator.generator import Generator +from c3.signal.gates import Instruction from c3.utils.tf_utils import ( tf_kron, tf_matmul_left, @@ -8,12 +12,46 @@ tf_spost, ) +unitary_provider = dict() +state_provider = dict() + def step_vonNeumann_psi(psi, h, dt): return -1j * dt * tf.linalg.matvec(h, psi) -def gen_dUs_RK4(h0, hks, cflds_t, dt): +def unitary_deco(func): + """ + Decorator for making registry of functions + """ + unitary_provider[str(func.__name__)] = func + return func + + +def state_deco(func): + """ + Decorator for making registry of functions + """ + state_provider[str(func.__name__)] = func + return func + + +@unitary_deco +def gen_dUs_RK4(model: Model, gen: Generator, instr: Instruction, init_state=None): + h0, hctrls = model.get_Hamiltonians() + gen.resolution = 2 * gen.resolution + signal = gen.generate_signals(instr) + signals = [] + hks = [] + for key in signal: + signals.append(signal[key]["values"]) + ts = signal[key]["ts"] + hks.append(hctrls[key]) + signals = tf.cast(signals, tf.complex128) + hks = tf.cast(hks, tf.complex128) + + dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) + U = [] tot_dim = tf.shape(h0) dim = tot_dim[1] @@ -22,12 +60,11 @@ def gen_dUs_RK4(h0, hks, cflds_t, dt): h = h0 ii = 0 while ii < len(hks): - h += cflds_t[ii] * hks[ii] + h += signals[ii] * hks[ii] ii += 1 else: h = h0 - for jj in range(0, len(h) - 2, 2): dU = [] for ii in range(dim): @@ -45,15 +82,44 @@ def gen_dUs_RK4(h0, hks, cflds_t, dt): return U -def gen_U_RK4(h, dt): +@unitary_deco +def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None): + gen.resolution = 2 * gen.resolution + signal = gen.generate_signals(instr) + if model.controllability: + h0, hctrls = model.get_Hamiltonians() + + signals = [] + hks = [] + for key in signal: + signals.append(signal[key]["values"]) + ts = signal[key]["ts"] + hks.append(hctrls[key]) + signals = tf.cast(signals, tf.complex128) + hks = tf.cast(hks, tf.complex128) + + dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) + + U = [] + tot_dim = tf.shape(h0) + dim = tot_dim[1] + + if hks is not None: + h = h0 + ii = 0 + while ii < len(hks): + h += signals[ii] * hks[ii] + ii += 1 + + else: + h = model.get_Hamiltonian(signal) - dU = [] tot_dim = tf.shape(h) dim = tot_dim[1] for ii in range(dim): psi = tf.one_hot(ii, dim, dtype=tf.complex128) - for jj in range(len(h), 2): + for jj in range(0, len(h) - 2, 2): k1 = step_vonNeumann_psi(psi, h[jj], dt) k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[jj + 1], dt) @@ -61,9 +127,150 @@ def gen_U_RK4(h, dt): k4 = step_vonNeumann_psi(psi + k3, h[jj + 2], dt) psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 - dU.append(psi) - dU = tf.stack(dU) - return tf.transpose(dU) + U.append(psi) + U = tf.stack(U) + return tf.transpose(U) + + +@unitary_deco +def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: + """ + Solve the equation of motion (Lindblad or Schrödinger) for a given control + signal and Hamiltonians. + + Parameters + ---------- + signal: dict + Waveform of the control signal per drive line. + gate: str + Identifier for one of the gates. + + Returns + ------- + unitary + Matrix representation of the gate. + """ + signal = gen.generate_signals(instr) + if model.controllability: + h0, hctrls = model.get_Hamiltonians() + signals = [] + hks = [] + for key in signal: + signals.append(signal[key]["values"]) + ts = signal[key]["ts"] + hks.append(hctrls[key]) + signals = tf.cast(signals, tf.complex128) + hks = tf.cast(hks, tf.complex128) + else: + h0 = model.get_Hamiltonian(signal) + ts_list = [sig["ts"][1:] for sig in signal.values()] + ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) + signals = None + hks = None + assert np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])) + assert np.all( + tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) + ) + + dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) + + batch_size = tf.constant(len(h0), tf.int32) + + dUs = tf_batch_propagate(h0, hks, signals, dt, batch_size=batch_size) + + U = tf_matmul_left(tf.cast(dUs, tf.complex128)) + + if model.max_excitations: + U = model.blowup_excitations(tf_matmul_left(tf.cast(dUs, tf.complex128))) + dUs = tf.vectorized_map(model.blowup_excitations, dUs) + + return {"U": U, "dUs": dUs, "ts": ts} + + +# Reference for cutting excitations: +# if model.max_excitations: +# cutter = model.ex_cutter +# hamiltonian = cutter @ hamiltonian @ cutter.T +# if hks is not None: +# cutter_tf = tf.cast(cutter, tf.complex128) +# hks = tf.matmul(cutter_tf, tf.matmul(hks, cutter_tf, transpose_b=True)) + +# dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) + +# if model.lindbladian: +# col_ops = model.get_Lindbladians() +# if model.max_excitations: +# cutter = model.ex_cutter +# col_ops = [cutter @ col_op @ cutter.T for col_op in col_ops] +# dUs = tf_propagation_lind(hamiltonian, hks, col_ops, signals, dt) +# else: +# batch_size = ( +# self.propagate_batch_size +# if self.propagate_batch_size +# else len(hamiltonian) +# ) +# batch_size = ( +# len(hamiltonian) if batch_size > len(hamiltonian) else batch_size +# ) +# batch_size = tf.constant(batch_size, tf.int32) +# dUs = tf_batch_propagate( +# hamiltonian, hks, signals, dt, batch_size=batch_size +# ) + +# U = tf_matmul_left(tf.cast(dUs, tf.complex128)) +# if model.max_excitations: +# U = cutter.T @ U @ cutter +# ex_cutter = tf.cast(tf.expand_dims(model.ex_cutter, 0), tf.complex128) +# if self.stop_partial_propagator_gradient: +# self.partial_propagators[gate] = tf.stop_gradient( +# tf.linalg.matmul( +# tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), +# ex_cutter, +# ) +# ) +# else: +# self.partial_propagators[gate] = tf.stop_gradient( +# tf.linalg.matmul( +# tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), +# ex_cutter, +# ) +# ) +# else: +# self.partial_propagators[gate] = dUs + +# self.ts = ts +# return U + + +# @state_deco +# def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Dict[str, List[tf.Tensor]]: +# if init_state is None: +# init_state = model.get_ground_state() + +# key = list(signal.keys())[0] +# ts = signal[key]["ts"] +# dt = ts[1] - ts[0] + +# states = [init_state] +# for i in range(len(ts)): +# signals = [] +# for key in signal: +# signals.append(signal[key]["values"][i]) +# states.append(rk4_solver_step(model, signals, dt, states[-1])) +# return {"states": states} + + +# def rk4_solver_step(model: Model, signal, dt: float, state) -> tf.Tensor: +# k1 = model.eom(state, signal) +# k2 = model.eom(state + dt * k1 / 2.0, signal) +# k3 = model.eom(state + dt * k2 / 2.0, signal) +# k4 = model.eom(state + dt * k3, signal) +# return state + dt * (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 + + +#################### +# HELPER FUNCTIONS # +#################### @tf.function @@ -96,7 +303,7 @@ def tf_dU_of_t(h0, hks, cflds_t, dt): # terms = int(1e12 * dt) + 2 # dU = tf_expm(-1j * h * dt, terms) # TODO Make an option for the exponentation method - dU = gen_U_RK4(h, dt) # tf.linalg.expm(-1j * h * dt) + dU = tf.linalg.expm(-1j * h * dt) return dU @@ -209,7 +416,6 @@ def tf_batch_propagate(hamiltonian, hks, signals, dt, batch_size): batch_array = batch_array.write( i, signals[i * batch_size : i * batch_size + batch_size] ) - else: batches = int(tf.math.ceil(hamiltonian.shape[0] / batch_size)) batch_array = tf.TensorArray( @@ -224,13 +430,14 @@ def tf_batch_propagate(hamiltonian, hks, signals, dt, batch_size): for i in range(batches): x = batch_array.read(i) if signals is not None: - result = tf_propagation(hamiltonian, hks, x, dt) + result = tf_propagation_vectorized(hamiltonian, hks, x, dt) else: - result = tf_propagation(x, None, None, dt) + result = tf_propagation_vectorized(x, None, None, dt) dUs_array = dUs_array.write(i, result) return dUs_array.concat() +@unitary_deco def tf_propagation(h0, hks, cflds, dt): """ Calculate the unitary time evolution of a system controlled by time-dependent @@ -259,7 +466,7 @@ def tf_propagation(h0, hks, cflds, dt): cf_t = [] for fields in cflds: cf_t.append(tf.cast(fields[ii], tf.complex128)) - dUs.append(tf_dU_of_t(h0, hks, cf_t, dt)) # + dUs.append(tf_dU_of_t(h0, hks, cf_t, dt)) return dUs @@ -301,7 +508,7 @@ def tf_propagation_lind(h0, hks, col_ops, cflds_t, dt, history=False): return dU -def evaluate_sequences(propagators: dict, sequences: list): +def evaluate_sequences(propagators: Dict, sequences: list): """ Compute the total propagator of a sequence of gates. From 9092007ac86f66dc9021d26afdeab6c0c6e7c7a3 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:14:00 +0100 Subject: [PATCH 30/80] added possibility to choose propagation methods and exp.prop deleted --- c3/experiment.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/c3/experiment.py b/c3/experiment.py index f7ef95af..70ceb4e9 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -54,7 +54,7 @@ class Experiment: """ - def __init__(self, pmap: ParameterMap = None, prop_method = None): + def __init__(self, pmap: ParameterMap = None, prop_method=None): self.pmap = pmap self.opt_gates = None self.propagators: Dict[str, tf.Tensor] = {} @@ -430,7 +430,7 @@ def compute_states(self) -> Dict[Instruction, List[tf.Tensor]]: f" Available gates are:\n {list(instructions.keys())}." ) signal = generator.generate_signals(instr) - result = self..pyagation(model, signal) + result = self.propagation(model, signal) states[instr] = result["states"] self.states = states return result @@ -462,6 +462,8 @@ def compute_propagators(self): f"C3:Error: Gate '{gate}' is not defined." f" Available gates are:\n {list(instructions.keys())}." ) + + model.controllability = self.use_control_fields result = self.propagation(model, generator, instr) U = result["U"] dUs = result["dUs"] From f224f241bab17a0bd6221d444cee8b6097707188 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:18:06 +0100 Subject: [PATCH 31/80] config for prop method included --- test/c1.cfg | 1 + 1 file changed, 1 insertion(+) diff --git a/test/c1.cfg b/test/c1.cfg index 6a3f1ed3..7f8f800c 100644 --- a/test/c1.cfg +++ b/test/c1.cfg @@ -6,6 +6,7 @@ "fid_func": "average_infid_set", "fid_subspace": ["Q1"], "algorithm" : "lbfgs", + "propagation_method": "pwc", "options" : { "maxfun": 2 } From 2542c9d54bcf68abf664ba024f58388534e42267 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:22:44 +0100 Subject: [PATCH 32/80] modified test_get_Hamiltonian to avoid sparse matrices --- test/test_transmon_expanded.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/test/test_transmon_expanded.py b/test/test_transmon_expanded.py index 63364057..702427d7 100644 --- a/test/test_transmon_expanded.py +++ b/test/test_transmon_expanded.py @@ -216,6 +216,7 @@ def test_chip_hamiltonians(): @pytest.mark.integration def test_hamiltonians(): + model.max_excitations = 0 test_data["hamiltonians_q1"] = model.get_Hamiltonian(gen_signal1) test_data["hamiltonians_q2"] = model.get_Hamiltonian(gen_signal2) @@ -234,6 +235,7 @@ def test_hamiltonians(): @pytest.mark.integration def test_propagation(): + model.max_excitations = cut_excitations exp.set_opt_gates("instr1") exp.compute_propagators() test_data["propagators_q1"] = exp.propagators["instr1"] From 58d80bd02b8ab4bdc036eef0f9c7d2a42e835b9b Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:35:12 +0100 Subject: [PATCH 33/80] modified call of the propagators --- test/test_two_qubits.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/test/test_two_qubits.py b/test/test_two_qubits.py index 036f4278..4fe4d451 100644 --- a/test/test_two_qubits.py +++ b/test/test_two_qubits.py @@ -327,7 +327,8 @@ gen_signal = generator.generate_signals(pmap.instructions["rx90p[0]"]) ts = gen_signal["d1"]["ts"] hdrift, hks = model.get_Hamiltonians() -propagator = exp.propagation(gen_signal, "rx90p[0]") +result = exp.propagation(model, generator, pmap.instructions["rx90p[0]"]) +propagator = result["U"] def test_signals() -> None: From d1999e88319e356deec42f11a64c50c3420fa2ad Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 10 Nov 2021 17:35:45 +0100 Subject: [PATCH 34/80] testing for prop method included --- test/test_optim_init.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/test/test_optim_init.py b/test/test_optim_init.py index 87cea304..06e3aae3 100644 --- a/test/test_optim_init.py +++ b/test/test_optim_init.py @@ -49,8 +49,7 @@ def test_create_c1() -> None: cfg.pop("optim_type") cfg.pop("gateset_opt_map") cfg.pop("opt_gates") - - exp = Experiment() + exp = Experiment(prop_method=cfg.pop("propagation_method", None)) exp.read_config(cfg.pop("exp_cfg")) OptimalControl(**cfg, pmap=exp.pmap) From dca32616848dfe7c7890e8727bbc0b13cdc7d520 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Thu, 11 Nov 2021 17:50:17 +0100 Subject: [PATCH 35/80] Renamed method for a consistent load/store pair. --- c3/optimizers/optimizer.py | 2 +- c3/parametermap.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 56df2059..51d7acaa 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -171,7 +171,7 @@ def log_parameters(self) -> None: if self.optim_status["goal"] < self.current_best_goal: self.current_best_goal = self.optim_status["goal"] self.current_best_params = self.optim_status["params"] - self.pmap.write_values( + self.pmap.store_values( path=self.logdir + "best_point_" + self.logname, optim_status=self.optim_status, ) diff --git a/c3/parametermap.py b/c3/parametermap.py index d6b65d4a..48a23ded 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -81,7 +81,7 @@ def load_values(self, init_point): init_p = best["optim_status"]["params"] self.set_parameters(init_p, best_opt_map) - def write_values(self, path: str, optim_status=None) -> None: + def store_values(self, path: str, optim_status=None) -> None: """ Write current parameter values to file. From 7d9eb64ad7b53db2646d9bfbd26b4663b21956bf Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 12 Nov 2021 11:02:34 +0100 Subject: [PATCH 36/80] Added docstring. --- c3/parametermap.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/c3/parametermap.py b/c3/parametermap.py index 48a23ded..9557f8f3 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -83,12 +83,16 @@ def load_values(self, init_point): def store_values(self, path: str, optim_status=None) -> None: """ - Write current parameter values to file. + Write current parameter values to file. Stores the numeric values, as well as the names + in form of the opt_map and physical units. If an optim_status is given that will be + used. Parameters ---------- path : str Location of the resulting logfile. + optim_status: dict + Dictionary containing current parameters and goal function value. """ if optim_status is None: optim_status = { From 7c8dc49defb2cc7c34a330c7cfeed19c184c0f30 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 12 Nov 2021 11:52:23 +0100 Subject: [PATCH 37/80] Pmap tutorial added. --- examples/Parametermap.ipynb | 549 ++++++++++++++++++++++++++++++++++++ 1 file changed, 549 insertions(+) create mode 100644 examples/Parametermap.ipynb diff --git a/examples/Parametermap.ipynb b/examples/Parametermap.ipynb new file mode 100644 index 00000000..56db3e5c --- /dev/null +++ b/examples/Parametermap.ipynb @@ -0,0 +1,549 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Parameter handling\n", + "The tool within $C^3$ to manipulate the parameters of both the model and controls is the `ParameterMap`. It provides methods to present the same data for human interaction, i.e. structured information with physical units and for numerical optimization algorithms that prefer a linear vector of scale 1. Here, we'll show some example usage.\n", + "We'll use the `ParameterMap` of the model also used in the simulated calibration example." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "from single_qubit_blackbox_exp import create_experiment\n", + "\n", + "exp = create_experiment()\n", + "pmap = exp.pmap" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The pmap contains a list of all parameters and their values:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'Q1-freq': 5.000 GHz 2pi,\n", + " 'Q1-anhar': -210.000 MHz 2pi,\n", + " 'Q1-temp': 0.000 K,\n", + " 'init_ground-init_temp': -3.469 aK,\n", + " 'resp-rise_time': 300.000 ps,\n", + " 'v_to_hz-V_to_Hz': 1.000 GHz/V,\n", + " 'id[0]-d1-no_drive-amp': 1.000 V,\n", + " 'id[0]-d1-no_drive-delta': 0.000 V,\n", + " 'id[0]-d1-no_drive-freq_offset': 0.000 Hz 2pi,\n", + " 'id[0]-d1-no_drive-xy_angle': 0.000 rad,\n", + " 'id[0]-d1-no_drive-sigma': 5.000 ns,\n", + " 'id[0]-d1-no_drive-t_final': 7.000 ns,\n", + " 'id[0]-d1-carrier-freq': 5.050 GHz 2pi,\n", + " 'id[0]-d1-carrier-framechange': 5.933 rad,\n", + " 'rx90p[0]-d1-gauss-amp': 450.000 mV,\n", + " 'rx90p[0]-d1-gauss-delta': -1.000 ,\n", + " 'rx90p[0]-d1-gauss-freq_offset': -50.500 MHz 2pi,\n", + " 'rx90p[0]-d1-gauss-xy_angle': -444.089 arad,\n", + " 'rx90p[0]-d1-gauss-sigma': 1.750 ns,\n", + " 'rx90p[0]-d1-gauss-t_final': 7.000 ns,\n", + " 'rx90p[0]-d1-carrier-freq': 5.050 GHz 2pi,\n", + " 'rx90p[0]-d1-carrier-framechange': 0.000 rad,\n", + " 'ry90p[0]-d1-gauss-amp': 450.000 mV,\n", + " 'ry90p[0]-d1-gauss-delta': -1.000 ,\n", + " 'ry90p[0]-d1-gauss-freq_offset': -50.500 MHz 2pi,\n", + " 'ry90p[0]-d1-gauss-xy_angle': 1.571 rad,\n", + " 'ry90p[0]-d1-gauss-sigma': 1.750 ns,\n", + " 'ry90p[0]-d1-gauss-t_final': 7.000 ns,\n", + " 'ry90p[0]-d1-carrier-freq': 5.050 GHz 2pi,\n", + " 'ry90p[0]-d1-carrier-framechange': 0.000 rad,\n", + " 'rx90m[0]-d1-gauss-amp': 450.000 mV,\n", + " 'rx90m[0]-d1-gauss-delta': -1.000 ,\n", + " 'rx90m[0]-d1-gauss-freq_offset': -50.500 MHz 2pi,\n", + " 'rx90m[0]-d1-gauss-xy_angle': 3.142 rad,\n", + " 'rx90m[0]-d1-gauss-sigma': 1.750 ns,\n", + " 'rx90m[0]-d1-gauss-t_final': 7.000 ns,\n", + " 'rx90m[0]-d1-carrier-freq': 5.050 GHz 2pi,\n", + " 'rx90m[0]-d1-carrier-framechange': 0.000 rad,\n", + " 'ry90m[0]-d1-gauss-amp': 450.000 mV,\n", + " 'ry90m[0]-d1-gauss-delta': -1.000 ,\n", + " 'ry90m[0]-d1-gauss-freq_offset': -50.500 MHz 2pi,\n", + " 'ry90m[0]-d1-gauss-xy_angle': 4.712 rad,\n", + " 'ry90m[0]-d1-gauss-sigma': 1.750 ns,\n", + " 'ry90m[0]-d1-gauss-t_final': 7.000 ns,\n", + " 'ry90m[0]-d1-carrier-freq': 5.050 GHz 2pi,\n", + " 'ry90m[0]-d1-carrier-framechange': 0.000 rad}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_full_params()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To access a specific parameter, e.g. the frequency of qubit 1, we use the identifying tuple `('Q1','freq')`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "5.000 GHz 2pi" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameter(('Q1','freq'))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## The opt_map\n", + "To deal with multiple parameters we use the `opt_map`, a nested list of identifyers." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "opt_map = [\n", + " [\n", + " (\"Q1\", \"freq\")\n", + " ],\n", + " [\n", + " (\"Q1\", \"anhar\")\n", + " ], \n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, we get a list of the parameter values:" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[5.000 GHz 2pi, -210.000 MHz 2pi]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters(opt_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the amplitude values of two gaussian control pulses, rotations about the $X$ and $Y$ axes repsectively." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "opt_map = [\n", + " [\n", + " ('rx90p[0]','d1','gauss','amp')\n", + " ],\n", + " [\n", + " ('ry90p[0]','d1','gauss','amp')\n", + " ], \n", + "]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[450.000 mV, 450.000 mV]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters(opt_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can set the parameters to new values." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.set_parameters([0.5, 0.6], opt_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[500.000 mV, 600.000 mV]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters(opt_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The opt_map also allows us to specify that two parameters should have identical values. Here, let's demand our $X$ and $Y$ rotations use the same amplitude." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "opt_map_ident = [\n", + " [\n", + " ('rx90p[0]','d1','gauss','amp'),\n", + " ('ry90p[0]','d1','gauss','amp')\n", + " ],\n", + "]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The grouping here means that these parameters share their numerical value." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[432.000 mV]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.set_parameters([0.432], opt_map_ident)\n", + "pmap.get_parameters(opt_map_ident)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[432.000 mV, 432.000 mV]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters(opt_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "During an optimization, the varied parameters do not change, so we fix the opt_map" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.set_opt_map(opt_map)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[432.000 mV, 432.000 mV]" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizer scaling\n", + "To be independent of the choice of numerical optimizer, they should use the methods" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.68, -0.68])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters_scaled()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To provide values bound to $[-1, 1]$. Let's set the parameters to their allowed minimum an maximum value with" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.set_parameters_scaled([1.0,-1.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[600.000 mV, 400.000 mV]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a safeguard, when setting values outside of the unit range, their physical values get looped back in the specified limits." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.set_parameters_scaled([2.0, 3.0])" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[500.000 mV, 400.000 mV]" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pmap.get_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Storing and reading\n", + "For optimization purposes, we can store and load parameter values in [HJSON](https://hjson.github.io/) format." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.store_values(\"current_vals.c3log\")" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{\r\n", + " opt_map:\r\n", + " [\r\n", + " [\r\n", + " rx90p[0]-d1-gauss-amp\r\n", + " ]\r\n", + " [\r\n", + " ry90p[0]-d1-gauss-amp\r\n", + " ]\r\n", + " ]\r\n", + " units:\r\n", + " [\r\n", + " V\r\n", + " V\r\n", + " ]\r\n", + " optim_status:\r\n", + " {\r\n", + " params:\r\n", + " [\r\n", + " 0.5\r\n", + " 0.4000000059604645\r\n", + " ]\r\n", + " }\r\n", + "}\r\n" + ] + } + ], + "source": [ + "!cat current_vals.c3log" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "pmap.load_values(\"current_vals.c3log\")" + ] + } + ], + "metadata": { + "interpreter": { + "hash": "8fc56ae400e717d872a76f4d6b257151d16696a9d0a72e6998d355f9b43887c7" + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 9a9128a77bf68f7b3e49133999a3727b1f09d494 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 12 Nov 2021 11:54:33 +0100 Subject: [PATCH 38/80] Parametermap tutorial added to docs. --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 21da5fa8..e7e50d42 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -28,6 +28,7 @@ When combined in sequence, these three procedures represent a recipe for system :caption: Contents: intro + Parametermap two_qubits optimal_control Simulated_calibration From a8a2432b1f70317fae267d38c0ad7b731ae649b1 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 12 Nov 2021 16:18:03 +0100 Subject: [PATCH 39/80] Tutorial for pmap in docs --- docs/Parametermap.rst | 340 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 340 insertions(+) create mode 100644 docs/Parametermap.rst diff --git a/docs/Parametermap.rst b/docs/Parametermap.rst new file mode 100644 index 00000000..b22fcb32 --- /dev/null +++ b/docs/Parametermap.rst @@ -0,0 +1,340 @@ +Parameter handling +================== + +The tool within :math:`C^3` to manipulate the parameters of both the +model and controls is the ``ParameterMap``. It provides methods to +present the same data for human interaction, i.e. structured information +with physical units and for numerical optimization algorithms that +prefer a linear vector of scale 1. Here, we’ll show some example usage. +We’ll use the ``ParameterMap`` of the model also used in the simulated +calibration example. + +.. code-block:: python + + from single_qubit_blackbox_exp import create_experiment + + exp = create_experiment() + pmap = exp.pmap + +The pmap contains a list of all parameters and their values: + +.. code-block:: python + + pmap.get_full_params() + + + + +.. parsed-literal:: + + {'Q1-freq': 5.000 GHz 2pi, + 'Q1-anhar': -210.000 MHz 2pi, + 'Q1-temp': 0.000 K, + 'init_ground-init_temp': -3.469 aK, + 'resp-rise_time': 300.000 ps, + 'v_to_hz-V_to_Hz': 1.000 GHz/V, + 'id[0]-d1-no_drive-amp': 1.000 V, + 'id[0]-d1-no_drive-delta': 0.000 V, + 'id[0]-d1-no_drive-freq_offset': 0.000 Hz 2pi, + 'id[0]-d1-no_drive-xy_angle': 0.000 rad, + 'id[0]-d1-no_drive-sigma': 5.000 ns, + 'id[0]-d1-no_drive-t_final': 7.000 ns, + 'id[0]-d1-carrier-freq': 5.050 GHz 2pi, + 'id[0]-d1-carrier-framechange': 5.933 rad, + 'rx90p[0]-d1-gauss-amp': 450.000 mV, + 'rx90p[0]-d1-gauss-delta': -1.000 , + 'rx90p[0]-d1-gauss-freq_offset': -50.500 MHz 2pi, + 'rx90p[0]-d1-gauss-xy_angle': -444.089 arad, + 'rx90p[0]-d1-gauss-sigma': 1.750 ns, + 'rx90p[0]-d1-gauss-t_final': 7.000 ns, + 'rx90p[0]-d1-carrier-freq': 5.050 GHz 2pi, + 'rx90p[0]-d1-carrier-framechange': 0.000 rad, + 'ry90p[0]-d1-gauss-amp': 450.000 mV, + 'ry90p[0]-d1-gauss-delta': -1.000 , + 'ry90p[0]-d1-gauss-freq_offset': -50.500 MHz 2pi, + 'ry90p[0]-d1-gauss-xy_angle': 1.571 rad, + 'ry90p[0]-d1-gauss-sigma': 1.750 ns, + 'ry90p[0]-d1-gauss-t_final': 7.000 ns, + 'ry90p[0]-d1-carrier-freq': 5.050 GHz 2pi, + 'ry90p[0]-d1-carrier-framechange': 0.000 rad, + 'rx90m[0]-d1-gauss-amp': 450.000 mV, + 'rx90m[0]-d1-gauss-delta': -1.000 , + 'rx90m[0]-d1-gauss-freq_offset': -50.500 MHz 2pi, + 'rx90m[0]-d1-gauss-xy_angle': 3.142 rad, + 'rx90m[0]-d1-gauss-sigma': 1.750 ns, + 'rx90m[0]-d1-gauss-t_final': 7.000 ns, + 'rx90m[0]-d1-carrier-freq': 5.050 GHz 2pi, + 'rx90m[0]-d1-carrier-framechange': 0.000 rad, + 'ry90m[0]-d1-gauss-amp': 450.000 mV, + 'ry90m[0]-d1-gauss-delta': -1.000 , + 'ry90m[0]-d1-gauss-freq_offset': -50.500 MHz 2pi, + 'ry90m[0]-d1-gauss-xy_angle': 4.712 rad, + 'ry90m[0]-d1-gauss-sigma': 1.750 ns, + 'ry90m[0]-d1-gauss-t_final': 7.000 ns, + 'ry90m[0]-d1-carrier-freq': 5.050 GHz 2pi, + 'ry90m[0]-d1-carrier-framechange': 0.000 rad} + + + +To access a specific parameter, e.g. the frequency of qubit 1, we use +the identifying tuple ``('Q1','freq')``. + +.. code-block:: python + + pmap.get_parameter(('Q1','freq')) + + + + +.. parsed-literal:: + + 5.000 GHz 2pi + + + +The opt_map +----------- + +To deal with multiple parameters we use the ``opt_map``, a nested list +of identifyers. + +.. code-block:: python + + opt_map = [ + [ + ("Q1", "freq") + ], + [ + ("Q1", "anhar") + ], + ] + +Here, we get a list of the parameter values: + +.. code-block:: python + + pmap.get_parameters(opt_map) + + + + +.. parsed-literal:: + + [5.000 GHz 2pi, -210.000 MHz 2pi] + + + +Let’s look at the amplitude values of two gaussian control pulses, +rotations about the :math:`X` and :math:`Y` axes repsectively. + +.. code-block:: python + + opt_map = [ + [ + ('rx90p[0]','d1','gauss','amp') + ], + [ + ('ry90p[0]','d1','gauss','amp') + ], + ] + +.. code-block:: python + + pmap.get_parameters(opt_map) + + + + +.. parsed-literal:: + + [450.000 mV, 450.000 mV] + + + +We can set the parameters to new values. + +.. code-block:: python + + pmap.set_parameters([0.5, 0.6], opt_map) + +.. code-block:: python + + pmap.get_parameters(opt_map) + + + + +.. parsed-literal:: + + [500.000 mV, 600.000 mV] + + + +The opt_map also allows us to specify that two parameters should have +identical values. Here, let’s demand our :math:`X` and :math:`Y` +rotations use the same amplitude. + +.. code-block:: python + + opt_map_ident = [ + [ + ('rx90p[0]','d1','gauss','amp'), + ('ry90p[0]','d1','gauss','amp') + ], + ] + +The grouping here means that these parameters share their numerical +value. + +.. code-block:: python + + pmap.set_parameters([0.432], opt_map_ident) + pmap.get_parameters(opt_map_ident) + + + + +.. parsed-literal:: + + [432.000 mV] + + + +.. code-block:: python + + pmap.get_parameters(opt_map) + + + + +.. parsed-literal:: + + [432.000 mV, 432.000 mV] + + + +During an optimization, the varied parameters do not change, so we fix +the opt_map + +.. code-block:: python + + pmap.set_opt_map(opt_map) + +.. code-block:: python + + pmap.get_parameters() + + + + +.. parsed-literal:: + + [432.000 mV, 432.000 mV] + + + +Optimizer scaling +----------------- + +To be independent of the choice of numerical optimizer, they should use +the methods + +.. code-block:: python + + pmap.get_parameters_scaled() + + + + +.. parsed-literal:: + + array([-0.68, -0.68]) + + + +To provide values bound to :math:`[-1, 1]`. Let’s set the parameters to +their allowed minimum an maximum value with + +.. code-block:: python + + pmap.set_parameters_scaled([1.0,-1.0]) + +.. code-block:: python + + pmap.get_parameters() + + + + +.. parsed-literal:: + + [600.000 mV, 400.000 mV] + + + +As a safeguard, when setting values outside of the unit range, their +physical values get looped back in the specified limits. + +.. code-block:: python + + pmap.set_parameters_scaled([2.0, 3.0]) + +.. code-block:: python + + pmap.get_parameters() + + + + +.. parsed-literal:: + + [500.000 mV, 400.000 mV] + + + +Storing and reading +------------------- + +For optimization purposes, we can store and load parameter values in +`HJSON `__ format. + +.. code-block:: python + + pmap.store_values("current_vals.c3log") + +.. code-block:: python + + !cat current_vals.c3log + + +.. parsed-literal:: + + { + opt_map: + [ + [ + rx90p[0]-d1-gauss-amp + ] + [ + ry90p[0]-d1-gauss-amp + ] + ] + units: + [ + V + V + ] + optim_status: + { + params: + [ + 0.5 + 0.4000000059604645 + ] + } + } + + +.. code-block:: python + + pmap.load_values("current_vals.c3log") From 5110fdce5c1b84bfdb2c7a243ed57a8845db01ca Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Mon, 15 Nov 2021 21:50:58 +0100 Subject: [PATCH 40/80] add recent PRs. Closes #152 --- CHANGELOG.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 2f52862a..6662f1a5 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,7 +13,10 @@ This Changelog tracks all past changes to this project as well as details about ### Details -- `fixed` handling of anharmonicity in transmons with two levels #145 +- `added` a method to HJSON dump current parameter values #149 +- `added` example for the log reader CLI #137 +- `added` human readable saving of current best point for the optimizer #140 +- `fixed` handling of anharmonicity in transmons with two levels #146 ## Version `1.3` - 20 Jul 2021 From efa06b0bd21b4ab8b2ffed386faba95cfa27c4b2 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Mon, 15 Nov 2021 22:51:05 +0100 Subject: [PATCH 41/80] remove redundant workflows. Closes #153 --- .github/workflows/build_package.yml | 4 ++-- .github/workflows/integration_test.yml | 33 -------------------------- 2 files changed, 2 insertions(+), 35 deletions(-) delete mode 100644 .github/workflows/integration_test.yml diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index d4631090..cfdeca2b 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -1,4 +1,4 @@ -name: Python Package Build +name: Build and Test on: push: @@ -8,7 +8,7 @@ on: workflow_dispatch: jobs: - build: + build-and-test: runs-on: ${{ matrix.os }} strategy: matrix: diff --git a/.github/workflows/integration_test.yml b/.github/workflows/integration_test.yml deleted file mode 100644 index 9d1b892b..00000000 --- a/.github/workflows/integration_test.yml +++ /dev/null @@ -1,33 +0,0 @@ -name: Integration Tests - -on: - push: - branches: [ master, dev, 'release/*' ] - pull_request: - branches: [ master, dev, 'release/*' ] - workflow_dispatch: - -jobs: - integration_unit_tests: - runs-on: ${{ matrix.os }} - strategy: - matrix: - os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9] - env: - OS: ${{ matrix.os }} - PYTHON: ${{ matrix.python-version }} - steps: - - uses: actions/checkout@v2 - - name: Set up Python ${{ matrix.python-version }} - uses: actions/setup-python@v2 - with: - python-version: ${{ matrix.python-version }} - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install -r requirements.txt - - name: Test with pytest - run: | - pytest -v -x -m "not slow" test/ - pytest -v -x -m "slow" test/ From 0a91ad421a70b15d46a86406389a778e29805ab2 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Tue, 16 Nov 2021 00:50:33 +0100 Subject: [PATCH 42/80] add citation data, closes #145 --- README.md | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/README.md b/README.md index 84ad21b5..730ab9dc 100755 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ [![codecov](https://codecov.io/gh/q-optimize/c3/branch/dev/graph/badge.svg)](https://codecov.io/gh/q-optimize/c3) [![Language grade: Python](https://img.shields.io/lgtm/grade/python/g/q-optimize/c3.svg?logo=lgtm&logoWidth=18)](https://lgtm.com/projects/g/q-optimize/c3/context:python) +[![Build and Test](https://github.com/q-optimize/c3/actions/workflows/build_package.yml/badge.svg)](https://github.com/q-optimize/c3/actions/workflows/build_package.yml) [![Documentation Status](https://readthedocs.org/projects/c3-toolset/badge/?version=latest)](https://c3-toolset.readthedocs.io/en/latest/?badge=latest) [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) [![PyPI version fury.io](https://badge.fury.io/py/c3-toolset.svg)](https://pypi.python.org/pypi/c3-toolset/) @@ -38,3 +39,20 @@ Examples are available in the `examples/` directory and can also be run online u If you wish to contribute, please check out the issues tab and also the `CONTRIBUTING.md` for useful resources. The source code is available on Github at [https://github.com/q-optimize/c3](https://github.com/q-optimize/c3). + +## Citation + +If you use `c3-toolset` in your research, please cite it as below: + +``` +@article{Wittler2021, + title={Integrated Tool Set for Control, Calibration, and Characterization of Quantum Devices Applied to Superconducting Qubits}, + volume={15}, + DOI={10.1103/physrevapplied.15.034080}, + number={3}, + journal={Physical Review Applied}, + author={Wittler, Nicolas and Roy, Federico and Pack, Kevin and Werninghaus, Max and Saha Roy, Anurag and Egger, Daniel J. and Filipp, Stefan and Wilhelm, Frank K. and Machnes, Shai}, + year={2021}, + month={Mar} +} +``` \ No newline at end of file From 07ac716c7ca3253e384edfcbe338aaae33e04f6a Mon Sep 17 00:00:00 2001 From: frosati1 Date: Wed, 17 Nov 2021 10:26:30 +0100 Subject: [PATCH 43/80] fixed exp.ts = result --- c3/experiment.py | 1 + 1 file changed, 1 insertion(+) diff --git a/c3/experiment.py b/c3/experiment.py index 70ceb4e9..7dc14144 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -467,6 +467,7 @@ def compute_propagators(self): result = self.propagation(model, generator, instr) U = result["U"] dUs = result["dUs"] + self.ts = result["ts"] if model.use_FR: # TODO change LO freq to at the level of a line freqs = {} From 17b03bb3feb3deffe9d68c4e2ecdf326fbe2ddd5 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Tue, 30 Nov 2021 11:23:52 +0100 Subject: [PATCH 44/80] drop support for Python 3.6 --- .github/workflows/build_package.yml | 2 +- .github/workflows/nightly_pypi.yml | 2 +- .github/workflows/publish_pypi.yml | 2 +- CONTRIBUTING.md | 2 +- setup.py | 3 +-- 5 files changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/build_package.yml b/.github/workflows/build_package.yml index cfdeca2b..bfefca51 100644 --- a/.github/workflows/build_package.yml +++ b/.github/workflows/build_package.yml @@ -13,7 +13,7 @@ jobs: strategy: matrix: os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9] + python-version: [3.7, 3.8, 3.9] env: OS: ${{ matrix.os }} PYTHON: ${{ matrix.python-version }} diff --git a/.github/workflows/nightly_pypi.yml b/.github/workflows/nightly_pypi.yml index b5d27f5a..c74bdd4f 100644 --- a/.github/workflows/nightly_pypi.yml +++ b/.github/workflows/nightly_pypi.yml @@ -49,7 +49,7 @@ jobs: strategy: matrix: os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9] + python-version: [3.7, 3.8, 3.9] env: OS: ${{ matrix.os }} PYTHON: ${{ matrix.python-version }} diff --git a/.github/workflows/publish_pypi.yml b/.github/workflows/publish_pypi.yml index f4fc5f8f..9447e2c3 100644 --- a/.github/workflows/publish_pypi.yml +++ b/.github/workflows/publish_pypi.yml @@ -45,7 +45,7 @@ jobs: strategy: matrix: os: [ubuntu-20.04, ubuntu-18.04, macos-latest, windows-latest] - python-version: [3.6, 3.7, 3.8, 3.9] + python-version: [3.7, 3.8, 3.9] env: OS: ${{ matrix.os }} PYTHON: ${{ matrix.python-version }} diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 55192e8c..c03fb93b 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -261,7 +261,7 @@ We use the [CHANGELOG.md](CHANGELOG.md) as a living document that not only recor ## Continuous Integration Checks -As previously discussed, tests are an inherent part of good code. All Pull Requests made to the repository are automatically tested on Windows, MacOS and Ubuntu with Python 3.6, 3.7, 3.8 and 3.9, which come up in the PR as `Checks`, notifying you whenever a particular check is failing on any platform. Except the `Code Complexity`, all checks should be passing for your PR to be ready for review/merge. The `Codecov` bot will also comment on the PR with a report on the change in the test coverage for that particular PR. If your PR reduces the overall test coverage of the codebase, it is not yet ready and you need to add more tests. +As previously discussed, tests are an inherent part of good code. All Pull Requests made to the repository are automatically tested on Windows, MacOS and Ubuntu with Python 3.7, 3.8 and 3.9, which come up in the PR as `Checks`, notifying you whenever a particular check is failing on any platform. Except the `Code Complexity`, all checks should be passing for your PR to be ready for review/merge. The `Codecov` bot will also comment on the PR with a report on the change in the test coverage for that particular PR. If your PR reduces the overall test coverage of the codebase, it is not yet ready and you need to add more tests. ## Contributor License Agreement diff --git a/setup.py b/setup.py index 21d8e7ac..8cf1503c 100644 --- a/setup.py +++ b/setup.py @@ -28,7 +28,6 @@ "Programming Language :: Python :: 3.9", "Programming Language :: Python :: 3.8", "Programming Language :: Python :: 3.7", - "Programming Language :: Python :: 3.6", "Topic :: Scientific/Engineering :: Artificial Intelligence", "Topic :: Scientific/Engineering :: Physics", ], @@ -46,5 +45,5 @@ "tensorflow-estimator>=2.4.0", "tensorflow-probability>=0.12.1", ], - python_requires="~=3.6", + python_requires="~=3.7", ) From 669a215d038c3bc9a8d7bd6d89b09859ccdc444b Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Tue, 30 Nov 2021 11:35:44 +0100 Subject: [PATCH 45/80] add note on removing support for python 3.6 --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 6662f1a5..22311dfa 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ This Changelog tracks all past changes to this project as well as details about ### Details +- `removed` official support for Python 3.6 #156 - `added` a method to HJSON dump current parameter values #149 - `added` example for the log reader CLI #137 - `added` human readable saving of current best point for the optimizer #140 From 65fdd83eeb8cdfac8f6bac953d507225286171d0 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Mon, 29 Nov 2021 14:45:28 +0100 Subject: [PATCH 46/80] example notebook for two-qubit entangling gates --- examples/two_qubit_entangling_gate.ipynb | 769 +++++++++++++++++++++++ 1 file changed, 769 insertions(+) create mode 100644 examples/two_qubit_entangling_gate.ipynb diff --git a/examples/two_qubit_entangling_gate.ipynb b/examples/two_qubit_entangling_gate.ipynb new file mode 100644 index 00000000..a4a4e598 --- /dev/null +++ b/examples/two_qubit_entangling_gate.ipynb @@ -0,0 +1,769 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Entangling gate on two coupled qubits" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# !pip install -q -U pip\n", + "# !pip install -q matplotlib" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "# System imports\n", + "import copy\n", + "import numpy as np\n", + "import time\n", + "import itertools\n", + "import matplotlib.pyplot as plt\n", + "import tensorflow as tf\n", + "import tensorflow_probability as tfp\n", + "from typing import List\n", + "\n", + "# Main C3 objects\n", + "from c3.c3objs import Quantity as Qty\n", + "from c3.parametermap import ParameterMap as PMap\n", + "from c3.experiment import Experiment as Exp\n", + "from c3.model import Model as Mdl\n", + "from c3.generator.generator import Generator as Gnr\n", + "\n", + "# Building blocks\n", + "import c3.generator.devices as devices\n", + "import c3.signal.gates as gates\n", + "import c3.libraries.chip as chip\n", + "import c3.signal.pulse as pulse\n", + "import c3.libraries.tasks as tasks\n", + "\n", + "# Libs and helpers\n", + "import c3.libraries.algorithms as algorithms\n", + "import c3.libraries.hamiltonians as hamiltonians\n", + "import c3.libraries.fidelities as fidelities\n", + "import c3.libraries.envelopes as envelopes\n", + "import c3.utils.qt_utils as qt_utils\n", + "import c3.utils.tf_utils as tf_utils" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Model components\n", + "The model consists of two qubits with 3 levels each and slightly different parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "qubit_lvls = 3\n", + "freq_q1 = 5e9\n", + "anhar_q1 = -210e6\n", + "t1_q1 = 27e-6\n", + "t2star_q1 = 39e-6\n", + "qubit_temp = 50e-3\n", + "\n", + "q1 = chip.Qubit(\n", + " name=\"Q1\",\n", + " desc=\"Qubit 1\",\n", + " freq=Qty(value=freq_q1, min_val=4.995e9, max_val=5.005e9, unit='Hz 2pi'),\n", + " anhar=Qty(value=anhar_q1, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'),\n", + " hilbert_dim=qubit_lvls,\n", + " t1=Qty(value=t1_q1, min_val=1e-6, max_val=90e-6, unit='s'),\n", + " t2star=Qty(value=t2star_q1, min_val=10e-6, max_val=90e-3, unit='s'),\n", + " temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K')\n", + ")\n", + "\n", + "freq_q2 = 5.6e9\n", + "anhar_q2 = -240e6\n", + "t1_q2 = 23e-6\n", + "t2star_q2 = 31e-6\n", + "q2 = chip.Qubit(\n", + " name=\"Q2\",\n", + " desc=\"Qubit 2\",\n", + " freq=Qty(value=freq_q2, min_val=5.595e9, max_val=5.605e9, unit='Hz 2pi'),\n", + " anhar=Qty(value=anhar_q2, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'),\n", + " hilbert_dim=qubit_lvls,\n", + " t1=Qty(value=t1_q2, min_val=1e-6, max_val=90e-6,unit='s'),\n", + " t2star=Qty(value=t2star_q2, min_val=10e-6, max_val=90e-6, unit='s'),\n", + " temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K')\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "There is a static coupling in x-direction between them: $(b_1+b_1^\\dagger)(b_2+b_2^\\dagger)$" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "coupling_strength = 50e6\n", + "q1q2 = chip.Coupling(\n", + " name=\"Q1-Q2\",\n", + " desc=\"coupling\",\n", + " comment=\"Coupling qubit 1 to qubit 2\",\n", + " connected=[\"Q1\", \"Q2\"],\n", + " strength=Qty(\n", + " value=coupling_strength,\n", + " min_val=-1 * 1e3 ,\n", + " max_val=200e6 ,\n", + " unit='Hz 2pi'\n", + " ),\n", + " hamiltonian_func=hamiltonians.int_XX\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and each qubit has a drive line" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "drive1 = chip.Drive(\n", + " name=\"d1\",\n", + " desc=\"Drive 1\",\n", + " comment=\"Drive line 1 on qubit 1\",\n", + " connected=[\"Q1\"],\n", + " hamiltonian_func=hamiltonians.x_drive\n", + ")\n", + "drive2 = chip.Drive(\n", + " name=\"d2\",\n", + " desc=\"Drive 2\",\n", + " comment=\"Drive line 2 on qubit 2\",\n", + " connected=[\"Q2\"],\n", + " hamiltonian_func=hamiltonians.x_drive\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All parts are collected in the model. The initial state will be thermal at a non-vanishing temperature." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "init_temp = 50e-3\n", + "init_ground = tasks.InitialiseGround(\n", + " init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit='K')\n", + ")\n", + "\n", + "model = Mdl(\n", + " [q1, q2], # Individual, self-contained components\n", + " [drive1, drive2, q1q2], # Interactions between components\n", + " [init_ground] # SPAM processing\n", + ")\n", + "model.set_lindbladian(False)\n", + "model.set_dressed(True)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Control signals\n", + "The devices for the control line are set up" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "sim_res = 100e9 # Resolution for numerical simulation\n", + "awg_res = 2e9 # Realistic, limited resolution of an AWG\n", + "v2hz = 1e9\n", + "\n", + "lo = devices.LO(name='lo', resolution=sim_res)\n", + "awg = devices.AWG(name='awg', resolution=awg_res)\n", + "mixer = devices.Mixer(name='mixer')\n", + "resp = devices.Response(\n", + " name='resp',\n", + " rise_time=Qty(value=0.3e-9, min_val=0.05e-9, max_val=0.6e-9, unit='s'),\n", + " resolution=sim_res\n", + ")\n", + "dig_to_an = devices.DigitalToAnalog(name=\"dac\", resolution=sim_res)\n", + "v_to_hz = devices.VoltsToHertz(\n", + " name='v_to_hz',\n", + " V_to_Hz=Qty(value=v2hz, min_val=0.9e9, max_val=1.1e9, unit='Hz/V')\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The generator combines the parts of the signal generation and assignes a signal chain to each control line." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "generator = Gnr(\n", + " devices={\n", + " \"LO\": lo,\n", + " \"AWG\": awg,\n", + " \"DigitalToAnalog\": dig_to_an,\n", + " \"Response\": resp,\n", + " \"Mixer\": mixer,\n", + " \"VoltsToHertz\": v_to_hz\n", + " },\n", + " chains={\n", + " \"d1\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"],\n", + " \"d2\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"]\n", + " }\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Gates-set and Parameter map\n", + "Both qubits will be resonantly driven with a Gaussian envelope." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "t_final = 7e-9\n", + "sideband = 50e6 \n", + "gauss_params_single = {\n", + " 'amp': Qty(value=0.5, min_val=0.2, max_val=0.6, unit=\"V\"),\n", + " 't_final': Qty(value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit=\"s\"),\n", + " 'sigma': Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit=\"s\"),\n", + " 'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'),\n", + " 'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'),\n", + " 'delta': Qty(value=-1, min_val=-5, max_val=3, unit=\"\")\n", + "}\n", + "\n", + "gauss_env_single_1 = pulse.Envelope(\n", + " name=\"gauss1\",\n", + " desc=\"Gaussian envelope on drive 1\",\n", + " params=gauss_params_single,\n", + " shape=envelopes.gaussian_nonorm\n", + ")\n", + "gauss_env_single_2 = pulse.Envelope(\n", + " name=\"gauss2\",\n", + " desc=\"Gaussian envelope on drive 2\",\n", + " params=copy.deepcopy(gauss_params_single),\n", + " shape=envelopes.gaussian_nonorm\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The carrier signal of each drive is set to the resonance frequency\n", + "of the corresponding qubit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "lo_freq_q1 = freq_q1 + sideband\n", + "carr_1 = pulse.Carrier(\n", + " name=\"carrier\",\n", + " desc=\"Carrier on drive 1\",\n", + " params={\n", + " 'freq': Qty(value=lo_freq_q1, min_val=0.9 * lo_freq_q1, max_val=1.1 * lo_freq_q1, unit='Hz 2pi'),\n", + " 'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad')\n", + " }\n", + ")\n", + "\n", + "lo_freq_q2 = freq_q2 + sideband\n", + "carr_2 = pulse.Carrier(\n", + " name=\"carrier\",\n", + " desc=\"Carrier on drive 2\",\n", + " params={\n", + " 'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'),\n", + " 'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad')\n", + " }\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Instructions\n", + "The instruction to be optimised is a CNOT gates controlled by qubit 1." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "# CNOT comtrolled by qubit 1\n", + "cnot12 = gates.Instruction(\n", + " name=\"cnot12\", targets=[0, 1], t_start=0.0, t_end=t_final, channels=[\"d1\", \"d2\"],\n", + " ideal=np.array([\n", + " [1,0,0,0],\n", + " [0,1,0,0],\n", + " [0,0,0,1],\n", + " [0,0,1,0]\n", + " ])\n", + ")\n", + "cnot12.add_component(gauss_env_single_1, \"d1\")\n", + "cnot12.add_component(carr_1, \"d1\")\n", + "cnot12.add_component(gauss_env_single_2, \"d2\")\n", + "cnot12.add_component(carr_2, \"d2\")\n", + "cnot12.comps[\"d1\"][\"carrier\"].params[\"framechange\"].set_value(\n", + " (-sideband * t_final) * 2 * np.pi % (2 * np.pi)\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The experiment\n", + "All components are collected in the parameter map and the experiment is set up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "ExecuteTime": { + "end_time": "2020-06-10T13:45:05.684014Z", + "start_time": "2020-06-10T13:45:04.441825Z" + } + }, + "outputs": [], + "source": [ + "parameter_map = PMap(instructions=[cnot12], model=model, generator=generator)\n", + "exp = Exp(pmap=parameter_map)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Calculate and print the propagator before the optimisation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "unitaries = exp.compute_propagators()\n", + "print(unitaries[cnot12.get_key()])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Dynamics\n", + "\n", + "The system is initialised in the state $|0,1\\rangle$ so that a transition to $|1,1\\rangle$ should be visible." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "psi_init = [[0] * 9]\n", + "psi_init[0][1] = 1\n", + "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))\n", + "print(init_state)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "def plot_dynamics(exp, psi_init, seq, goal=-1):\n", + " \"\"\"\n", + " Plotting code for time-resolved populations.\n", + "\n", + " Parameters\n", + " ----------\n", + " psi_init: tf.Tensor\n", + " Initial state or density matrix.\n", + " seq: list\n", + " List of operations to apply to the initial state.\n", + " goal: tf.float64\n", + " Value of the goal function, if used.\n", + " debug: boolean\n", + " If true, return a matplotlib figure instead of saving.\n", + " \"\"\"\n", + " model = exp.pmap.model\n", + " dUs = exp.partial_propagators\n", + " psi_t = psi_init.numpy()\n", + " pop_t = exp.populations(psi_t, model.lindbladian)\n", + " for gate in seq:\n", + " for du in dUs[gate]:\n", + " psi_t = np.matmul(du.numpy(), psi_t)\n", + " pops = exp.populations(psi_t, model.lindbladian)\n", + " pop_t = np.append(pop_t, pops, axis=1)\n", + "\n", + " fig, axs = plt.subplots(1, 1)\n", + " ts = exp.ts\n", + " dt = ts[1] - ts[0]\n", + " ts = np.linspace(0.0, dt*pop_t.shape[1], pop_t.shape[1])\n", + " axs.plot(ts / 1e-9, pop_t.T)\n", + " axs.grid(linestyle=\"--\")\n", + " axs.tick_params(\n", + " direction=\"in\", left=True, right=True, top=True, bottom=True\n", + " )\n", + " axs.set_xlabel('Time [ns]')\n", + " axs.set_ylabel('Population')\n", + " plt.legend(model.state_labels)\n", + " pass\n", + "\n", + "def getQubitsPopulation(population: np.array, dims: List[int]) -> np.array:\n", + " \"\"\"\n", + " Splits the population of all levels of a system into the populations of levels per subsystem.\n", + " Parameters\n", + " ----------\n", + " population: np.array\n", + " The time dependent population of each energy level. First dimension: level index, second dimension: time.\n", + " dims: List[int]\n", + " The number of levels for each subsystem.\n", + " Returns\n", + " -------\n", + " np.array\n", + " The time-dependent population of energy levels for each subsystem. First dimension: subsystem index, second\n", + " dimension: level index, third dimension: time.\n", + " \"\"\"\n", + " numQubits = len(dims)\n", + "\n", + " # create a list of all levels\n", + " qubit_levels = []\n", + " for dim in dims:\n", + " qubit_levels.append(list(range(dim)))\n", + " combined_levels = list(itertools.product(*qubit_levels))\n", + "\n", + " # calculate populations\n", + " qubitsPopulations = np.zeros((numQubits, dims[0], population.shape[1]))\n", + " for idx, levels in enumerate(combined_levels):\n", + " for i in range(numQubits):\n", + " qubitsPopulations[i, levels[i]] += population[idx]\n", + " return qubitsPopulations\n", + "\n", + "def plotSplittedPopulation(\n", + " exp: Exp,\n", + " psi_init: tf.Tensor,\n", + " sequence: List[str],\n", + " filename: str = None\n", + ") -> None:\n", + " \"\"\"\n", + " Plots time dependent populations for multiple qubits in separate plots.\n", + " Parameters\n", + " ----------\n", + " exp: Experiment\n", + " The experiment containing the model and propagators\n", + " psi_init: np.array\n", + " Initial state vector\n", + " sequence: List[str]\n", + " List of gate names that will be applied to the state\n", + " -------\n", + " \"\"\"\n", + " # calculate the time dependent level population\n", + " model = exp.pmap.model\n", + " dUs = exp.partial_propagators\n", + " psi_t = psi_init.numpy()\n", + " pop_t = exp.populations(psi_t, model.lindbladian)\n", + " for gate in sequence:\n", + " for du in dUs[gate]:\n", + " psi_t = np.matmul(du, psi_t)\n", + " pops = exp.populations(psi_t, model.lindbladian)\n", + " pop_t = np.append(pop_t, pops, axis=1)\n", + " dims = [s.hilbert_dim for s in model.subsystems.values()]\n", + " splitted = getQubitsPopulation(pop_t, dims)\n", + "\n", + " # timestamps\n", + " dt = exp.ts[1] - exp.ts[0]\n", + " ts = np.linspace(0.0, dt * pop_t.shape[1], pop_t.shape[1])\n", + "\n", + " # create both subplots\n", + " fig, axs = plt.subplots(1, len(splitted), sharey=\"all\")\n", + " for idx, ax in enumerate(axs):\n", + " ax.plot(ts / 1e-9, splitted[idx].T)\n", + " ax.tick_params(direction=\"in\", left=True, right=True, top=False, bottom=True)\n", + " ax.set_xlabel(\"Time [ns]\")\n", + " ax.set_ylabel(\"Population\")\n", + " ax.legend([str(x) for x in np.arange(dims[idx])])\n", + " ax.grid()\n", + "\n", + " plt.tight_layout()\n", + " if filename:\n", + " plt.savefig(filename)\n", + " plt.show()\n", + "\n", + "sequence = [cnot12.get_key()]\n", + "plot_dynamics(exp, init_state, sequence)\n", + "#plot_dynamics(exp, init_state, sequence * 10)\n", + "plotSplittedPopulation(exp, init_state, sequence, filename=\"dynamics_before.png\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Open-loop optimal control\n", + "\n", + "Now, open-loop optimisation with DRAG enabled is set up." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "generator.devices['AWG'].enable_drag_2()\n", + "\n", + "opt_gates = [cnot12.get_key()]\n", + "exp.set_opt_gates(opt_gates)\n", + "\n", + "gateset_opt_map=[\n", + " [(cnot12.get_key(), \"d1\", \"gauss1\", \"amp\")],\n", + " [(cnot12.get_key(), \"d1\", \"gauss1\", \"freq_offset\")],\n", + " [(cnot12.get_key(), \"d1\", \"gauss1\", \"xy_angle\")],\n", + " [(cnot12.get_key(), \"d1\", \"gauss1\", \"delta\")],\n", + " [(cnot12.get_key(), \"d1\", \"carrier\", \"framechange\")],\n", + " [(cnot12.get_key(), \"d2\", \"gauss2\", \"amp\")],\n", + " [(cnot12.get_key(), \"d2\", \"gauss2\", \"freq_offset\")],\n", + " [(cnot12.get_key(), \"d2\", \"gauss2\", \"xy_angle\")],\n", + " [(cnot12.get_key(), \"d2\", \"gauss2\", \"delta\")],\n", + " [(cnot12.get_key(), \"d2\", \"carrier\", \"framechange\")]\n", + "]\n", + "parameter_map.set_opt_map(gateset_opt_map)\n", + "\n", + "parameter_map.print_parameters()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As a fidelity function we choose average fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "import tempfile\n", + "from c3.optimizers.optimalcontrol import OptimalControl\n", + "\n", + "log_dir = os.path.join(tempfile.TemporaryDirectory().name, \"c3logs\")\n", + "opt = OptimalControl(\n", + " dir_path=log_dir,\n", + " fid_func=fidelities.average_infid_set,\n", + " fid_subspace=[\"Q1\", \"Q2\"],\n", + " pmap=parameter_map,\n", + " algorithm=algorithms.cmaes,\n", + " options={\n", + " \"popsize\": 15,\n", + " \"maxfevals\": 1000\n", + " },\n", + " run_name=\"cnot12\"\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start the optimisation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "exp.set_opt_gates(opt_gates)\n", + "opt.set_exp(exp)\n", + "opt.optimize_controls()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The final parameters and the fidelity are" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "parameter_map.print_parameters()\n", + "print(opt.current_best_goal)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Results of the optimisation\n", + "Plotting the dynamics with the same initial state:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "plot_dynamics(exp, init_state, sequence)\n", + "plotSplittedPopulation(exp, init_state, sequence, filename=\"dynamics_after.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + } + ], + "metadata": { + "interpreter": { + "hash": "8fc56ae400e717d872a76f4d6b257151d16696a9d0a72e6998d355f9b43887c7" + }, + "kernelspec": { + "display_name": "Python 3.8.8 64-bit ('venv': conda)", + "name": "python3" + }, + "language_info": { + "name": "python", + "version": "" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} \ No newline at end of file From 4e1963e32d7418eaffd7665a39e966fdfa3f4154 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Mon, 6 Dec 2021 22:27:56 +0100 Subject: [PATCH 47/80] outputs and reduced maxfevals --- examples/two_qubit_entangling_gate.ipynb | 290 ++++++++++++++++++----- 1 file changed, 233 insertions(+), 57 deletions(-) diff --git a/examples/two_qubit_entangling_gate.ipynb b/examples/two_qubit_entangling_gate.ipynb index a4a4e598..f0c10534 100644 --- a/examples/two_qubit_entangling_gate.ipynb +++ b/examples/two_qubit_entangling_gate.ipynb @@ -16,22 +16,21 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "# !pip install -q -U pip\n", - "# !pip install -q matplotlib" - ], + "execution_count": 1, "metadata": { - "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [ + "# !pip install -q -U pip\n", + "# !pip install -q matplotlib" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -83,7 +82,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -135,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -169,7 +168,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -203,7 +202,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": { + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [], "source": [ "init_temp = 50e-3\n", @@ -218,13 +222,7 @@ ")\n", "model.set_lindbladian(False)\n", "model.set_dressed(True)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", @@ -236,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -273,7 +271,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -308,7 +306,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -352,7 +350,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -392,7 +390,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -430,7 +428,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": { "ExecuteTime": { "end_time": "2020-06-10T13:45:05.684014Z", @@ -452,9 +450,62 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[ 2.90040286e-01+0.06796916j -9.05419295e-02-0.44005323j\n", + " 3.32932691e-02+0.02624818j 1.83699230e-02-0.4519875j\n", + " -6.87075560e-01+0.11948446j 3.30388750e-02-0.07559797j\n", + " 5.12968176e-02+0.03489245j 4.00691980e-02-0.06469375j\n", + " 2.56872145e-03+0.01350305j]\n", + " [-1.31456151e-01-0.43111727j 2.26936794e-01-0.20111687j\n", + " 1.91084183e-02-0.01480484j -6.51684826e-01+0.24723886j\n", + " -2.47467811e-01-0.3840622j -3.20181777e-02-0.00415827j\n", + " 1.46942329e-02-0.08169747j 4.25011406e-02+0.00106457j\n", + " -4.06200000e-03+0.00186351j]\n", + " [ 3.78678020e-02-0.00179018j 2.76809420e-03-0.02315965j\n", + " -4.13538094e-01-0.33772374j -9.69947972e-03-0.07244919j\n", + " -5.30975369e-02+0.01107233j -2.52346729e-01+0.79596642j\n", + " 8.22126812e-03+0.00440004j -3.55403231e-04-0.00818356j\n", + " -3.93164689e-02-0.07564104j]\n", + " [-3.46355245e-01+0.29125939j 5.89368247e-01+0.37203659j\n", + " -6.51961274e-02+0.00734386j -2.31070430e-01-0.182206j\n", + " -2.55521484e-01+0.38035364j -2.33895953e-02-0.05274812j\n", + " -2.81575528e-02+0.02641964j 3.08826166e-02+0.05269581j\n", + " 3.29263144e-04+0.00563204j]\n", + " [ 5.91929721e-01+0.36442811j -1.10400754e-01+0.44309809j\n", + " 2.93850138e-02+0.04873209j -2.12723842e-01+0.40645443j\n", + " -2.76142184e-01-0.11497212j -2.11191992e-02+0.03398605j\n", + " 3.62913577e-02+0.03056929j -2.37617310e-02+0.01633795j\n", + " -5.92571600e-03+0.01999525j]\n", + " [-4.93145510e-02+0.07007041j 2.49429187e-02+0.00473892j\n", + " 7.64049641e-01-0.33605327j -5.20743576e-02-0.03270246j\n", + " 5.71777817e-03+0.0273477j 3.81010149e-01+0.37777146j\n", + " 2.96257183e-03+0.00452817j -4.96636736e-03+0.00508481j\n", + " 5.14540480e-02+0.01186775j]\n", + " [-1.79229744e-02+0.06387697j 9.01881856e-02-0.00591019j\n", + " -7.40758132e-03+0.00576899j 2.92000888e-02-0.01302864j\n", + " -1.45902044e-02-0.03226958j 1.15117575e-03-0.00514899j\n", + " 2.37089484e-01-0.46819796j -7.27638732e-01-0.41711908j\n", + " 7.41651067e-02-0.0258729j ]\n", + " [ 8.29132270e-02+0.00837308j 1.59064811e-02+0.04661615j\n", + " 9.64368638e-03+0.00170216j -1.38408342e-02-0.04443097j\n", + " 1.61003315e-02-0.00407821j 7.04992162e-03+0.00120346j\n", + " -7.71513780e-01-0.32662086j 2.32631427e-01-0.47838175j\n", + " -2.43539508e-02-0.05065904j]\n", + " [-1.18817139e-03+0.00218983j 1.08118605e-02-0.00401908j\n", + " 6.04382648e-02-0.06681276j 1.36747414e-02-0.00111907j\n", + " 7.32825969e-03+0.01292438j -3.65918454e-02-0.02609693j\n", + " 4.43034429e-02-0.07812736j -3.15525737e-02-0.01770467j\n", + " -9.41975117e-01+0.30433893j]], shape=(9, 9), dtype=complex128)\n" + ] + } + ], "source": [ "unitaries = exp.compute_propagators()\n", "print(unitaries[cnot12.get_key()])" @@ -471,9 +522,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[0.+0.j]\n", + " [1.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]], shape=(9, 1), dtype=complex128)\n" + ] + } + ], "source": [ "psi_init = [[0] * 9]\n", "psi_init[0][1] = 1\n", @@ -483,9 +551,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "def plot_dynamics(exp, psi_init, seq, goal=-1):\n", " \"\"\"\n", @@ -623,9 +716,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cnot12[0, 1]-d1-gauss1-amp : 500.000 mV \n", + "cnot12[0, 1]-d1-gauss1-freq_offset : -53.000 MHz 2pi \n", + "cnot12[0, 1]-d1-gauss1-xy_angle : -444.089 arad \n", + "cnot12[0, 1]-d1-gauss1-delta : -1.000 \n", + "cnot12[0, 1]-d1-carrier-framechange : 4.084 rad \n", + "cnot12[0, 1]-d2-gauss2-amp : 500.000 mV \n", + "cnot12[0, 1]-d2-gauss2-freq_offset : -53.000 MHz 2pi \n", + "cnot12[0, 1]-d2-gauss2-xy_angle : -444.089 arad \n", + "cnot12[0, 1]-d2-gauss2-delta : -1.000 \n", + "cnot12[0, 1]-d2-carrier-framechange : 0.000 rad \n", + "\n" + ] + } + ], "source": [ "generator.devices['AWG'].enable_drag_2()\n", "\n", @@ -658,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -667,6 +778,7 @@ "from c3.optimizers.optimalcontrol import OptimalControl\n", "\n", "log_dir = os.path.join(tempfile.TemporaryDirectory().name, \"c3logs\")\n", + "# increase maxfevals to 500+ for better results\n", "opt = OptimalControl(\n", " dir_path=log_dir,\n", " fid_func=fidelities.average_infid_set,\n", @@ -675,7 +787,7 @@ " algorithm=algorithms.cmaes,\n", " options={\n", " \"popsize\": 15,\n", - " \"maxfevals\": 1000\n", + " \"maxfevals\": 100\n", " },\n", " run_name=\"cnot12\"\n", ")" @@ -690,9 +802,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C3:STATUS:Saving as: /tmp/tmpskwo3xlm/c3logs/cnot12/2021_12_06_T_22_24_59/open_loop.log\n", + "(7_w,15)-aCMA-ES (mu_w=4.5,w_1=34%) in dimension 10 (seed=964937, Mon Dec 6 22:24:59 2021)\n", + "Iterat #Fevals function value axis ratio sigma min&max std t[m:s]\n", + " 1 15 7.326344256215179e-01 1.0e+00 8.85e-02 8e-02 9e-02 0:13.8\n", + " 2 30 7.367559004446577e-01 1.2e+00 8.75e-02 8e-02 1e-01 0:27.4\n", + " 3 45 7.139157246650509e-01 1.3e+00 9.55e-02 9e-02 1e-01 0:40.8\n", + " 4 60 6.952586691625754e-01 1.5e+00 1.04e-01 9e-02 1e-01 0:54.5\n", + " 5 75 6.691554889600820e-01 1.8e+00 1.13e-01 1e-01 2e-01 1:08.0\n", + " 6 90 6.642503941891256e-01 1.9e+00 1.22e-01 1e-01 2e-01 1:21.7\n", + " 7 105 6.754441193654891e-01 1.9e+00 1.22e-01 1e-01 2e-01 1:35.7\n", + "termination on maxfevals=100\n", + "final/bestever f-value = 6.754441e-01 6.642504e-01\n", + "incumbent solution: [ 0.29354993 0.43400975 -0.62646119 0.04613593 -0.31716057 0.26852775\n", + " 0.54834962 -0.64817835 ...]\n", + "std deviations: [0.12406951 0.11915925 0.1206521 0.11160312 0.16109297 0.13345903\n", + " 0.11291629 0.10293621 ...]\n" + ] + } + ], "source": [ "exp.set_opt_gates(opt_gates)\n", "opt.set_exp(exp)\n", @@ -708,9 +843,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cnot12[0, 1]-d1-gauss1-amp : 461.437 mV \n", + "cnot12[0, 1]-d1-gauss1-freq_offset : -52.704 MHz 2pi \n", + "cnot12[0, 1]-d1-gauss1-xy_angle : 355.730 mrad \n", + "cnot12[0, 1]-d1-gauss1-delta : -1.058 \n", + "cnot12[0, 1]-d1-carrier-framechange : 827.654 mrad \n", + "cnot12[0, 1]-d2-gauss2-amp : 428.475 mV \n", + "cnot12[0, 1]-d2-gauss2-freq_offset : -53.037 MHz 2pi \n", + "cnot12[0, 1]-d2-gauss2-xy_angle : 73.836 mrad \n", + "cnot12[0, 1]-d2-gauss2-delta : -484.207 m \n", + "cnot12[0, 1]-d2-carrier-framechange : 120.479 mrad \n", + "\n", + "0.6642503941891256\n" + ] + } + ], "source": [ "parameter_map.print_parameters()\n", "print(opt.current_best_goal)" @@ -726,29 +880,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": { "pycharm": { "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plot_dynamics(exp, init_state, sequence)\n", "plotSplittedPopulation(exp, init_state, sequence, filename=\"dynamics_after.png\")" ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } } ], "metadata": { @@ -756,14 +923,23 @@ "hash": "8fc56ae400e717d872a76f4d6b257151d16696a9d0a72e6998d355f9b43887c7" }, "kernelspec": { - "display_name": "Python 3.8.8 64-bit ('venv': conda)", + "display_name": "Python 3 (ipykernel)", + "language": "python", "name": "python3" }, "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", "name": "python", - "version": "" + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.12" } }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From c80f92c224d60607a56060a62ef44e0a69e056a1 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Wed, 8 Dec 2021 12:37:37 +0100 Subject: [PATCH 48/80] update two qubit example notebook with cross resonance --- examples/two_qubit_entangling_gate.ipynb | 381 +++++++++++++++-------- 1 file changed, 254 insertions(+), 127 deletions(-) diff --git a/examples/two_qubit_entangling_gate.ipynb b/examples/two_qubit_entangling_gate.ipynb index f0c10534..86d5947c 100644 --- a/examples/two_qubit_entangling_gate.ipynb +++ b/examples/two_qubit_entangling_gate.ipynb @@ -24,8 +24,8 @@ }, "outputs": [], "source": [ - "# !pip install -q -U pip\n", - "# !pip install -q matplotlib" + "!pip install -q -U pip\n", + "!pip install -q matplotlib" ] }, { @@ -37,7 +37,16 @@ "start_time": "2020-06-10T13:45:04.441825Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-12-08 12:26:53.528802: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory\n", + "2021-12-08 12:26:53.528832: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" + ] + } + ], "source": [ "# System imports\n", "import copy\n", @@ -89,7 +98,19 @@ "start_time": "2020-06-10T13:45:04.441825Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-12-08 12:26:55.601657: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set\n", + "2021-12-08 12:26:55.601864: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'libcuda.so.1'; dlerror: libcuda.so.1: cannot open shared object file: No such file or directory\n", + "2021-12-08 12:26:55.601877: W tensorflow/stream_executor/cuda/cuda_driver.cc:326] failed call to cuInit: UNKNOWN ERROR (303)\n", + "2021-12-08 12:26:55.601899: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (localhost.localdomain): /proc/driver/nvidia/version does not exist\n", + "2021-12-08 12:26:55.602374: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set\n" + ] + } + ], "source": [ "qubit_lvls = 3\n", "freq_q1 = 5e9\n", @@ -301,7 +322,7 @@ "metadata": {}, "source": [ "#### Gates-set and Parameter map\n", - "Both qubits will be resonantly driven with a Gaussian envelope." + "Following a general cross resonance scheme, both qubits will be resonantly driven at the frequency of qubit 2 with a Gaussian envelope. We drive qubit 1 (the control) at the frequency of qubit 2 (the target) with a higher amplitude to compensate for the reduced Rabi frequency." ] }, { @@ -315,10 +336,19 @@ }, "outputs": [], "source": [ - "t_final = 7e-9\n", - "sideband = 50e6 \n", - "gauss_params_single = {\n", - " 'amp': Qty(value=0.5, min_val=0.2, max_val=0.6, unit=\"V\"),\n", + "t_final = 45e-9\n", + "sideband = 50e6\n", + "gauss_params_single_1 = {\n", + " 'amp': Qty(value=0.8, min_val=0.2, max_val=3, unit=\"V\"),\n", + " 't_final': Qty(value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit=\"s\"),\n", + " 'sigma': Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit=\"s\"),\n", + " 'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'),\n", + " 'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'),\n", + " 'delta': Qty(value=-1, min_val=-5, max_val=3, unit=\"\")\n", + "}\n", + "\n", + "gauss_params_single_2 = {\n", + " 'amp': Qty(value=0.03, min_val=0.02, max_val=0.6, unit=\"V\"),\n", " 't_final': Qty(value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit=\"s\"),\n", " 'sigma': Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit=\"s\"),\n", " 'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'),\n", @@ -329,13 +359,13 @@ "gauss_env_single_1 = pulse.Envelope(\n", " name=\"gauss1\",\n", " desc=\"Gaussian envelope on drive 1\",\n", - " params=gauss_params_single,\n", + " params=gauss_params_single_1,\n", " shape=envelopes.gaussian_nonorm\n", ")\n", "gauss_env_single_2 = pulse.Envelope(\n", " name=\"gauss2\",\n", " desc=\"Gaussian envelope on drive 2\",\n", - " params=copy.deepcopy(gauss_params_single),\n", + " params=gauss_params_single_2,\n", " shape=envelopes.gaussian_nonorm\n", ")" ] @@ -345,7 +375,7 @@ "metadata": {}, "source": [ "The carrier signal of each drive is set to the resonance frequency\n", - "of the corresponding qubit." + "of the target qubit." ] }, { @@ -360,16 +390,17 @@ "outputs": [], "source": [ "lo_freq_q1 = freq_q1 + sideband\n", + "lo_freq_q2 = freq_q2 + sideband\n", + "\n", "carr_1 = pulse.Carrier(\n", " name=\"carrier\",\n", " desc=\"Carrier on drive 1\",\n", " params={\n", - " 'freq': Qty(value=lo_freq_q1, min_val=0.9 * lo_freq_q1, max_val=1.1 * lo_freq_q1, unit='Hz 2pi'),\n", + " 'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'),\n", " 'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad')\n", " }\n", ")\n", "\n", - "lo_freq_q2 = freq_q2 + sideband\n", "carr_2 = pulse.Carrier(\n", " name=\"carrier\",\n", " desc=\"Carrier on drive 2\",\n", @@ -453,56 +484,64 @@ "execution_count": 13, "metadata": {}, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "2021-12-08 12:27:01.534483: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:116] None of the MLIR optimization passes are enabled (registered 2)\n", + "2021-12-08 12:27:01.551557: I tensorflow/core/platform/profile_utils/cpu_utils.cc:112] CPU Frequency: 2793495000 Hz\n" + ] + }, { "name": "stdout", "output_type": "stream", "text": [ "tf.Tensor(\n", - "[[ 2.90040286e-01+0.06796916j -9.05419295e-02-0.44005323j\n", - " 3.32932691e-02+0.02624818j 1.83699230e-02-0.4519875j\n", - " -6.87075560e-01+0.11948446j 3.30388750e-02-0.07559797j\n", - " 5.12968176e-02+0.03489245j 4.00691980e-02-0.06469375j\n", - " 2.56872145e-03+0.01350305j]\n", - " [-1.31456151e-01-0.43111727j 2.26936794e-01-0.20111687j\n", - " 1.91084183e-02-0.01480484j -6.51684826e-01+0.24723886j\n", - " -2.47467811e-01-0.3840622j -3.20181777e-02-0.00415827j\n", - " 1.46942329e-02-0.08169747j 4.25011406e-02+0.00106457j\n", - " -4.06200000e-03+0.00186351j]\n", - " [ 3.78678020e-02-0.00179018j 2.76809420e-03-0.02315965j\n", - " -4.13538094e-01-0.33772374j -9.69947972e-03-0.07244919j\n", - " -5.30975369e-02+0.01107233j -2.52346729e-01+0.79596642j\n", - " 8.22126812e-03+0.00440004j -3.55403231e-04-0.00818356j\n", - " -3.93164689e-02-0.07564104j]\n", - " [-3.46355245e-01+0.29125939j 5.89368247e-01+0.37203659j\n", - " -6.51961274e-02+0.00734386j -2.31070430e-01-0.182206j\n", - " -2.55521484e-01+0.38035364j -2.33895953e-02-0.05274812j\n", - " -2.81575528e-02+0.02641964j 3.08826166e-02+0.05269581j\n", - " 3.29263144e-04+0.00563204j]\n", - " [ 5.91929721e-01+0.36442811j -1.10400754e-01+0.44309809j\n", - " 2.93850138e-02+0.04873209j -2.12723842e-01+0.40645443j\n", - " -2.76142184e-01-0.11497212j -2.11191992e-02+0.03398605j\n", - " 3.62913577e-02+0.03056929j -2.37617310e-02+0.01633795j\n", - " -5.92571600e-03+0.01999525j]\n", - " [-4.93145510e-02+0.07007041j 2.49429187e-02+0.00473892j\n", - " 7.64049641e-01-0.33605327j -5.20743576e-02-0.03270246j\n", - " 5.71777817e-03+0.0273477j 3.81010149e-01+0.37777146j\n", - " 2.96257183e-03+0.00452817j -4.96636736e-03+0.00508481j\n", - " 5.14540480e-02+0.01186775j]\n", - " [-1.79229744e-02+0.06387697j 9.01881856e-02-0.00591019j\n", - " -7.40758132e-03+0.00576899j 2.92000888e-02-0.01302864j\n", - " -1.45902044e-02-0.03226958j 1.15117575e-03-0.00514899j\n", - " 2.37089484e-01-0.46819796j -7.27638732e-01-0.41711908j\n", - " 7.41651067e-02-0.0258729j ]\n", - " [ 8.29132270e-02+0.00837308j 1.59064811e-02+0.04661615j\n", - " 9.64368638e-03+0.00170216j -1.38408342e-02-0.04443097j\n", - " 1.61003315e-02-0.00407821j 7.04992162e-03+0.00120346j\n", - " -7.71513780e-01-0.32662086j 2.32631427e-01-0.47838175j\n", - " -2.43539508e-02-0.05065904j]\n", - " [-1.18817139e-03+0.00218983j 1.08118605e-02-0.00401908j\n", - " 6.04382648e-02-0.06681276j 1.36747414e-02-0.00111907j\n", - " 7.32825969e-03+0.01292438j -3.65918454e-02-0.02609693j\n", - " 4.43034429e-02-0.07812736j -3.15525737e-02-0.01770467j\n", - " -9.41975117e-01+0.30433893j]], shape=(9, 9), dtype=complex128)\n" + "[[ 5.38699071e-01-7.17750563e-02j -8.34752005e-01+8.73275022e-02j\n", + " -6.95346256e-03-2.15875540e-03j -4.35619589e-03+3.35449682e-03j\n", + " -1.06942994e-02+4.11831376e-03j -6.46672021e-05-3.73989900e-05j\n", + " -1.67838080e-04-2.08026492e-04j -6.43312053e-05-7.70584828e-07j\n", + " -3.76227149e-07-6.49845314e-07j]\n", + " [-8.22954017e-01+1.64865789e-01j -5.35373070e-01+9.17248769e-02j\n", + " -7.01716357e-03+7.68563193e-03j -1.04194796e-02+4.75452421e-03j\n", + " -1.61239175e-02-5.34774092e-03j -2.42060738e-04-1.19946128e-05j\n", + " 3.81855912e-05+8.66289943e-06j -1.30621879e-04-2.10380577e-04j\n", + " -8.82654253e-07-1.33276919e-06j]\n", + " [-7.61570279e-03+7.68089055e-04j -4.61417534e-03+9.02462832e-03j\n", + " 3.59132066e-01-9.32828470e-01j -9.10153028e-05-6.83262609e-05j\n", + " -2.24711912e-04+8.79671466e-05j 2.62921224e-02-1.48696337e-03j\n", + " -4.75883791e-04-4.20508543e-05j 3.46114778e-05+1.64470496e-04j\n", + " 2.10121296e-04+1.48066297e-04j]\n", + " [ 4.65531318e-03-6.63491197e-05j 8.62792565e-03+8.22022317e-03j\n", + " -5.58701973e-05+1.08666061e-04j 6.94902895e-02-7.11528641e-01j\n", + " -6.81737268e-01-1.53183314e-01j -2.09824678e-03-1.43761730e-03j\n", + " 1.48197730e-02-1.51149441e-02j -6.85074400e-03+1.43594091e-03j\n", + " 4.07440635e-05-6.43168354e-05j]\n", + " [ 9.49155432e-03+6.86731461e-03j 4.92068252e-03+1.60041286e-02j\n", + " 1.71300460e-04+1.83910737e-04j -6.94165643e-01-7.98008223e-02j\n", + " 1.68675369e-01-6.94722446e-01j 2.75768137e-03-5.72343874e-03j\n", + " -6.67593164e-03+1.87532770e-03j 1.07707017e-02+7.28665794e-03j\n", + " 1.40030301e-04-6.25646793e-05j]\n", + " [ 3.43460967e-05+8.01438338e-05j 1.86345824e-04+1.52916372e-04j\n", + " -1.74936595e-02-1.96833938e-02j -2.61695107e-03-5.33671505e-04j\n", + " 1.02116861e-03-6.21800378e-03j -4.07849502e-01+9.12571012e-01j\n", + " 7.51460471e-05-1.15167196e-04j 2.32056836e-04-2.97650209e-04j\n", + " 2.03278960e-04+1.15047574e-02j]\n", + " [ 2.54853797e-04-1.25904275e-04j 6.64845849e-05-1.08876861e-05j\n", + " 2.38628329e-04-2.95318799e-04j -2.10696691e-02+5.90348860e-05j\n", + " 4.21445291e-03+6.01993253e-03j -1.32690530e-04-2.44975772e-05j\n", + " 5.90859776e-01+4.84056180e-01j -6.08336007e-01-2.14442516e-01j\n", + " 3.13146026e-03+2.83895304e-03j]\n", + " [ 2.96366741e-05-8.10052801e-05j 2.39607442e-04-8.47647458e-05j\n", + " -2.60360838e-04+2.04175607e-04j 4.95127881e-03+5.19423708e-03j\n", + " -5.00047077e-03-1.18242204e-02j -3.71631612e-04-5.78977628e-05j\n", + " -6.29480118e-01-1.40758384e-01j -7.57820104e-01+9.68476237e-02j\n", + " 1.32060361e-03+7.25998662e-03j]\n", + " [ 8.28054635e-07-3.59336781e-07j 1.64602058e-06-1.47364829e-06j\n", + " -2.13361477e-04+2.05358711e-04j -5.70978380e-05+4.73283539e-05j\n", + " -1.48466829e-04-3.89352221e-06j 1.00811226e-02-5.54615336e-03j\n", + " 4.21887172e-03+1.38103179e-03j 3.74182763e-03+6.21303072e-03j\n", + " -5.89257172e-01+8.07818774e-01j]], shape=(9, 9), dtype=complex128)\n" ] } ], @@ -530,8 +569,8 @@ "output_type": "stream", "text": [ "tf.Tensor(\n", - "[[0.+0.j]\n", - " [1.+0.j]\n", + "[[1.+0.j]\n", + " [0.+0.j]\n", " [0.+0.j]\n", " [0.+0.j]\n", " [0.+0.j]\n", @@ -544,7 +583,7 @@ ], "source": [ "psi_init = [[0] * 9]\n", - "psi_init[0][1] = 1\n", + "psi_init[0][0] = 1\n", "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))\n", "print(init_state)" ] @@ -556,10 +595,8 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n" 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hBUopZQXeBi4COgMTlVKdT1rsKeBrrXUv4BpAutk4wUXdmhHoa+WpH7e6RZEKCAggNzfXpRug1prc3FwCAgLMjiLOJCgKHtwEPa+DrT9CVZnZiZqEp7YRR3aS6A9kaK13ASilpgLjgdqDM2ng2Nn6cGC/A/OIGiH+Prx8RTee+G4zt3+6huVPjsTPx3WP9iYlJZGVlcXhw4cbvY7y8nKHF4+AgACSkpIcug3RBJSCHtfAhi/gxQS4/ntoN9LsVGfFU9uIIwtUIlD7IG8WcPK4F88Bvyql7geCgfMdmEfUMr5nImEBvtz8yWoWph/mgs7xZkc6LV9fX5KTk89qHQsWLKBXr15NlEi4vVaD4Zx7YMU7sPR1ty9QntpGlKN2CZVSE4DRWuvbap5PAgZore+rtcxDNRleVUoNBD4EumqtT7hQISEhQYeHhx9/PmbMGMaOHXv8eXFxMSEhIQ75ORzFFTJX2zUPzC+la7SVu3vW75uTK+RuDGfnnj59OjNmzAAgPT19r9a6taO25YntA5yTu/XuL2i191uWDfqYKr+IJlmn/L7rp15tRGvtkBswEJhd6/mTwJMnLbMVaFHr+S4g7uR19enTR5/J/Pnzz/i+K3KVzM9N26JbPT5D3z9lna6qttW5vKvkbigzcwNrtIPamfbQ9qG1k3LnbNX62TDjtrNptie/74Y7XRtx5ImH1UB7pVSyUsoPoxPEtJOW2QeMBFBKpQABQOMPoooGe3RUR67oncS0jfuZkybDIAkvE98ZhjxsPF7yH3OziD9wWIHSWlcD9wGzgTSM3npblVIvKKXG1Sz2MHC7Umoj8CVwU001FU4S5Gd0mIgK9uPnzQfMjiOE8418xpiJd88SKHP9Xq3exKFDHWmtZwIzT3rtmVqPU4FzHZlB1M3XauH8lDh+2ZxDeZWNAN9TzwkjhMdKGQtLX4PpD8C4NyEgvM6PCMdz3b7Fwqku7ZVIUUU1l7+zjKx8mYFUeJnmvSGmA6T+BHNfMDuNqCEFSgAwqG0Mz43tzLacQv63cJfZcYRwLosF7lgI7c43LuC128xOJJACJWq56dxkRnVJYNbWHOx2ORUovIxfEPSYCKVHIGuN2WkEUqDESUZ3TeBwUQXrM+VksfBC7S8Aiw9MuRL2rTA7jdeTAiVOMKJTHL5WxetzMzhcVGF2HCGcKyAcRvwVygtg8b/NTuP1pECJE4QF+HLbkDYsSj/MQ19vMDuOEM435CEYeB/smg8VxWan8WpSoMQfPD66E/cMb8vSjCPklVSaHUcI52t/AdgqIeM3s5N4NSlQ4pRGdUnArmFhuowuIbxQi3MgIAK+uQlW/NfsNF5LCpQ4pW6J4cSE+DNXhj8S3sg3ACb9AIGRMkW8iaRAiVOyWBTnp8Qxc/MBXpuTbnYcIZwvsTcMfQwOp0H+HrPTeCUpUOK0HrqgA/2To3htzg4y82R0CeGF2l9g3C95DSpLTI3ijaRAidOKCwvg75d2A2BhugwyL7xQdDtoMQDWfgy/PGZ2Gq8jBUqcUdvYYJqHB7B8Z67ZUYRwPqXghp8geSikzQBbtdmJvIoUKHFGSikGtIlm5e5cbDL8kfBGvoHQ91YoPwpZq8xO41WkQIk6XdA5niPFlUx8fwVl1VKkhBdqO8IYAmnOc5Angyk7ixQoUafRXRJ4YGR7Vu3OY8V+OcQhvFBAOAy6HzJXwrQ/mZ3Ga0iBEnWyWBQPnt+eZuEBpOXJNAQn2DkfKqWHo1c4/zljevi9y2Tm3XrKK6lk8oq97DnSuB6QUqBEvSilGNgmmm15NrT28sN8dhssfQM+vBAmXwrL3jA7kXCW9qNA2yBjrtlJXJbWmkXph7njszUM/+d8nv5xC2v3Nq6gO3TKd+FZ+iVH8f36bHYdKaFtbIjZcZyvvBAW/B/sWgCHUqFZTxj6KJz7oMnBhNMk9YWgaJj9FwhLhFYDzU7kErTW7DpSwtdrMpm6KpOCsiqahQcwolMctw9pQ0qzsEatVwqUqLfB7WIAuOfzdXxwY19aRAWZnMhJ8nbBb8/A7kVQUWSM0zb+beh5ndENWXgPixUueRWmPwA/PwT3LDc7kalsds3m7AJe/iWNFbvyABjTvRnntovh8t6J+PtYz2r9UqBEvbWICuL6FD++Si/mrXkZvDKhu9mRHCtvF6z6ADZOAW2HlLHQ+0Zo0d/sZMJMXS6DwgMw+0ljCKTI1mYnMkVBWRU3fLiSjVkFBPpaeeqSFIZ1iKV9fGiTbUMKlGiQ81v5kmuNYknGEbOjONbBrTD5cuNkeKuBMOY/ENXG7FTCVXQcbRSo7bPgnLvMTuNUdrvmhRmpfLFyLwAvjO/C+SnxNI8IbPJtSYESDTYgOYpftuSQmVfqeYf5qsph6kTYOc8YyfrOhRCXYnYq4Wqi2kBsCsx9HoJjoNsEsxM5xW+pB/lg8S5W7s7jku7NuOXcZPq0inTY9qQXn2iw/snRAKzek2dykiZ2YBN8d6tRnEY8BXcvk+IkTu/SdyAkzhijz1ZldhqHOlhYzjsLMrhz8hp2Hi7hsdEdeWtiL4cWJ5A9KNEInRJCiQzy5aOlu+nTKpJW0cFmRzp7qz80Tnqj4MK/GxdlCnEmib1h9Mvw5TXGl5oOo8xO1OS0NjpB3PTxavJKKhnYJpoPb+pLkJ9zSofsQYkGMy7c7cCW7ELunbLO7Dhnp/gQzHoSfnkc2oyABzdJcRL113akcSh4zUfgYdcHFldUc+V/lzPuraVYLYrv7h7IF7cNcFpxAtmDEo1046DWVFbbeXFmGjkF5SSEB5gdqeGqyo1vvwc2QuvBMOEjCIoyO5VwJz5+0O1KWPUeTLsfxr9ldqIm8eGS3Xy2fA9Z+WX8+fwOXN2vhSltXPagRKMNbGuci1q+yw179K2bDO8Ng+y1cOUnxpQKUpxEY1zwN+g0BjZMgRI3bAu12OyaT5bu5m8zUgn0tfLudb154Pz2pn0BlQIlGq1zszDCA31ZsdPNOktkroJp94G9Gi5917i+SYjG8g2AYY8ZQyBt+9nsNI1WVq0Z/doinpueyqC20cy4fzAXdkkwNZMUKNFoFotiQHIUv6UdJO1Aodlx6qY1bPzK6KkXlgh3LISe15qdSniChO4QmQwL/o/wo1vNTtNgh4sq+GhLBRmHi3l2bGc+vLEfPlbzy4P5CYRbu3FQa4rKq7ji3WXklVSaHefMtv0MP9wBlSXGnpO/F44nKBxDKTjvKSgvpHPqv9xq5t28kkoueWMxq3Ns3D2sLTefm0yg39kNUdRUpECJs3Juuxim3H4OpZU2FqYfMjvO6WkNC18xvuU+nA5thpmdSHiabhPgsnfxr8yDPYvMTlMvm7MKuOeLtRwtreLB3v48Oqqj2ZFOIAVKnLU+LSNd+1zUkR30XvcY5GwyzhVYpfOqcJD2F1JtDYQV70JVmdlpzmhD5lEufWcpq3bn8fSYFHrG+aBcbPBjKVDirFksiv7JUSzdecQ154qa8xxBpfvgwheh+9VmpxGezDeQQ3FDYcevxiUMLkhrzXdrs7j3i3XEhPix4smRTBrY2uxYpyQFSjSJC1Liycov49Nle1yrSOXuhG0/k504FgbdZ0yXIIQDZbS7DfrcbMwbdmSH2XH+4OfNB3j4m41U2+28emVP4sJc9xpGhxYopdRopdR2pVSGUuqJ0yxzlVIqVSm1VSk1xZF5hOOM7pZAUmQgz01PZdaWHLPjGDZ+BR+NAh9/shMvMTuN8BJ2qx8Me9x4svUHc8PUUmWz8+LPqTz6zSa6Joax9PHzGNw+xuxYZ+SwAqWUsgJvAxcBnYGJSqnOJy3THngSOFdr3QV40FF5hGOFBfgy7+HhxIX6M23jfrPjQEUxzHwUQhLg2q+p9HfsoJZCnCCsGbQeAvNfhF9O+d3c6T5dtof3F++md6sIXr+ml0t0I69Lvc4WK6X8gSuA1rU/o7V+4Qwf6w9kaK131axjKjAeSK21zO3A21rr/Jr1uXA3MFEXPx8LI1PimL7xAFU2O75mNoANX0BFAYz5tzHB4L4F5mVpoEa2N+Fqxr8NP94DK/8LA++FiBamxKiy2Xnll21MXrGXoR1i+ewW95lwU9XnfIFSahZQAKwFbMde11q/eobPTABGa61vq3k+CRigtb6v1jI/AunAuYAVeE5rPevkdSUkJOjw8PDjz8eMGcPYsb9f/V9cXExIiHtd0+KOmaHu3OsOVvPG+goe7xdASrQJ53u0nc6prxJ7eBkF4Z3Y0PMlUMrpv+/p06czY8YMANLT0/dqrVvX97MNbW+e2D7AM3IHlB3knJV3sCt5EvtamTNn1Lx9VXyWWkmnKAu3dvUnNujUXxxdso1oreu8AVvqs9xJn5kAfFDr+STgrZOWmQH8APgCyUAmEHHyuvr06aPPZP78+Wd83xW5Y2at685dXF6l2/9lpr707SX6wNEy54SqbdsvWj8bpvWP92hdsP/4y2b+voE1umFtp0HtzRPbh9YelPuDC7X+W5zWC/7h9Cw7DhbpgS/N0ePeWqLtdvsZl3XFNlLfYzDLlFLdGlggs4Ha+7RJNa/VlgVM01pXaa13Y+xNtW/gdoQLCfb34c5hbVi/7ygvzHDykC92Gyz5jzGM0ZjXjPMA7qkx7U24qjH/gZj2sOAlOJrptM1uyDzKhf9ZSE5hOY9e2NHlrnGqj/oWqMHA2poeeZuUUpuVUpvq+MxqoL1SKlkp5QdcA0w7aZkfgeEASqkYoAOwq77hhWt6+MKO3DSoNXPTDlFWaav7A03hYCr8IxkyV8DgP4PV1znbdYzGtDfhquI7wzVTQNuNEc+d5MMluwny8+G3h4a5fG+906nvJfUXNXTFWutqpdR9wGyM80sfaa23KqVewNidm1bz3oVKqVSMY+2Paq1zG7ot4Xou6BzPJ8v2sCTjCBd0jnf8Bpe9aczvNP5t6OH2A8A2uL0JFxfREpKHGntR1eVw/rMO21SVzc6/Zm9n+sb93D4kmbax7nce75h67UFprfcCEcDYmltEzWt1fW6m1rqD1rqt1vrFmteeqSlO1Bx+fEhr3Vlr3U1rPbXRP4lwKf1aRxHq78Oc1IOO31jxYdj6PfS6DnpdDxbX7z57Jo1tb8LFjXkNWg2GJf82LiB3kK/XZPK/Rbvo2yqSO4e1ddh2nKFeLVkp9QDwBRBXc/tcKSXzYovT8vOxMKxjLF+tyeSNuQ68mj5tOrzZG6orYMDdjtuOE0l781DRbeGKD0BZYd1nDtmE1prJy/fSuVkY39w1kJgQf4dsx1nqe4jvVowu4iUASqlXgOXAm44KJtzf46M7kZlfxpvzdnDDwFZEBPk17Qa0hvkvgV+wcWgvtkPTrt880t48VVgz6DAalr4GZfkw9nVjqo4mkFNQzsT3V7D7SAn/urKHW3aKOFl9j4Uoal2PUfPY/X964VAtooL4+/iuVNk0v251wKG+nM1wKBWGPgKdxzX9+s0j7c2TXfIv6HgxrPsU9i1vstW+t2gX+/JK+cvFnbi8V2KTrddM9S1QHwMrlVLPKaWeA1YAHzoslfAYXRPDiA/zZ0FTzxWVvwfmPAtWf+hyedOu23zS3jxZWHO44kMIioalbzTJKlP3F/LV6n2M7pLAHUPbYrF4xveZ+naS+DdwM5BXc7tZa/2aA3MJD6GUYniHOOZvO8ysLQeaZqW2avj4Etg5DwbcAUFRTbNeFyHtzQv4BUG/2yH9F/h8AtjtjV7Vun35XPLmYiptdu5y804RJztjgVJKhdXcRwF7gM9rbntrXhOiTjed2xqrRXHX5+vYkHn07Fe4eyEUZsG4t+DCv5/9+lyEtDcvM/jP0PtGyPgNdi9o9Go+XLKbEH8ffv3zMLolhdf9ATdS1x7UsavK1gJrat2OPReiTinNwlj+5HkE+Vn5cuW+s1/hhingHw7drjz7dbkWaW/exDcALv6ncahv2VtQXdmgj2ut+XF9NjM3H+Dqvi1Ijgl2UFDznLFAaa3H1Nwna63b1Lola63bOCei8AShAb6cnxLPr6k5VNsaeTijvBA+HAVbvjWud/J13YnWGkPamxfy8YfeN8DOufDBecZwXfX08+YDPPjVBqKD/bl5cLIDQ5qnvtdBza3Pa0KcyUVdE8gvreKpH7dQWF7V8BVs+soYyqjfbTD88aYP6CKkvXmZ4U/C0MeMXqnbZtT7Y1+tziQxIpBlT5xHYkSgAwOap65zUAE1x75jlFKRSqmomltrwDP6MQqnGZkSz6gu8Uxdncm/f01v2Ie1Ni5uTOgGl7wKAZ51rB2kvXktH38Y/gSENoflb0NR3TNSr96Tx+IdR7iiTxJ+Pu49csqZ1PWT3Ylx/LtTzf2x20/AW46NJjyNn4+F/03qy+W9E/lmTWb9B5LN2wWTL4WcTdD3VodmNJm0N29lsRqTGmauhHfPNQ5nn8ai9MNc+d/lBPtZubqfOZMgOktd56Be11onA4/UOhaerLXuobWWBiMaZULvJEoqbcxJq+fFu3P/BrsWQM/roKfbDwR7WtLevNzAe+Hqz6H0iHER72l8tSaTyCBf5jw8zGMP7R1Tr6GOtNZvKqW6Ap2BgFqvO2ZAKeHRBrSJJjEikD9NXU9mfin3DG93+oWLcozx9s65B0b/n/NCmkjam5dSClLGQosBsPR1iGoDnS45YZGyShvz0g5xee9EmoV7dnGC+neSeBZjHLA3gRHAPwCPGltGOI/Vovjs1v70bx3Fa3N2cLio4tQLbvoa3h9pzKPT/3bnhjSRtDcvd/7zxv3U6+Dw7+dqiyuqefibDZRV2bi4m9tOxtkg9T27NgEYCeRorW8GegCed5ZaOE3b2BBevKwrldV2pm3c/8cFKkth5iNgrzI6RUR5VS9raW/erNVAuHu50Xli5bvHX/5g8S5mbs7h6r4tGNgm2sSAzlPfAlWmtbYD1TVXux/ixOnchWiwdnGhdEsM56Mlu1m288iJb275FsoLYMLH0PdmcwKaR9qbtwuJha5XwJqPYPqDYLcza0sO/ZOjeGVCd48Za68u9S1Qa5RSEcD7GL2K1mEM/y/EWXnowg7kl1Yy6cNV7DlSYnQnn/ci/PYMxHeDVoPMjmgGaW/CONSXMg7WfkzOuhlsyyliVJcEs1M5VX0Hi71Ha31Ua/1f4ALgxppDD0KclREd45j/yHCsSvHp8j2QvRYW/QNiU+DSd5psrhx3Iu1NAMZe1BUfYguOZ+fP/8ZqUYzu6l0F6oy9+JRSvc/0ntZ6XdNHEt4mPiyA8zrFMW3Dfp6yT8fqGwTXfgUBYWZHcyppb+IPfPxYGjGeoSXvsazlf4kPGm52Iqeqq5v5q2d4TwPnNWEW4cWu7t+Cw6kLqVr/JYUdxhPpZcWphrQ3cQKbXfP4wZE8GVHOuJzPYMt30HuS2bGc5owFSms9wllBhHcbblvBCP/nqcLKG8Xn8azZgUwg7U2c7Lu1WRwotuE77klYvBpWvAtdLgP/ELOjOUW9LtRVSt1wqtflwkHRVNTytyGiFR+0eZNPlhdzS14pLaKCzI5lCmlvAmD21hwe+24TiRGBnNc5HqrvhWn3wZu94a6lxjkqD1ffXnz9at2GAM8hFw6KppK/xxilvPckxg3tjwIe/GoD6QeLzE5mFmlvgi9W7iMiyJef/zQYfx+rMcXMVZOh+BCseNvseE5R36GO7q/9vKYL7FRHBBJeJmMOfHc7KAv0uJbE8EAevrAjr8/dwR2frWHuw8Oxesk1H8dIexMHC8tZsuMw9wxvR0SQn/GiUtB5nHGIb8W70PlSaN7TzJgO19hx2ksAz5whSziP3QY/3gtWP7j8fQg3ZpS4d0Q7/n1VD/bklrJg+yGTQ7oEaW9e5GBhOQ99vQG7hst7n2KWlSEPG9cLfjASDmx0fkAnqu85qOkYvYgArEAK8LWjQgkvsWcxFOfAlZ9Cl0tPeGtUlwTiQv15cWYaFotiRMc4czKaQNqbd3tpZhpLM3K5d0Rb2sSeojNEQle4Zzm8fx7MeR4mfe/8kE5SrwIF/KvW42pgr9Y6ywF5hLfIWgO/Pm1MPNhh1B/e9rVaePGybjwwdT23fLKa2Q8OpUN8qAlBTSHtzUuVV9mYk3qQif1b8OioTqdfMLqtsSf129Pw/R0w9nXw9bzRzes7ksRCYDvGgJVRGI1GiMYpyYVPxxoTEA55+LQN64LO8Sx5/DwCfKy8v2iXk0OaR9qb91qy4wgllTZGd63HaOX9bzfOR236Chb9q+7l3VB9p9u4DVgFXI4x0vIKpdQtjgwmPFjqD1BVCncshHMfOOOiUcF+XNorkR83ZPOf39KpqK7nLLxuTNqbd5qTepB7p6wjMsi3fqOV+wbClZ9At6uM+aO2zXR4Rmer7yG+R4FeWutcAKVUNLAM+MhRwYSHslXDuskQ2wma9ajXRx6+sAPbcwp5fe4OAv2s3DWsrYNDmk7amxd6bW460cF+vHN9H/x8GtB/7fznYMdsmDoRrv4CUsY4LKOz1fe3kAvUviilqOY1Ieovfy+82hEObIDeN9R7INiYEH++v+dcBraJ5rNle7DZdd0fcm/S3rzM/qNlbMkuZNLA1vRsEdGwD4cnwp82QHR7mP0XY5oaD1HfApUBrFRKPVcz2+cKIF0p9ZBS6iHHxRMeZc2HUJYHY/4D/e9o8McnDWzF/oJynvpxC3kllQ4I6DKkvXmZ31IPAnBB50b2Vg2KggtegKN74dVOcHBrE6YzT30L1E7gR37v+voTsBsIrbkJcWZaQ9p0aDMc+t4CVt8Gr2JUlwTG9mjOl6v28fh3m5o+o+uQ9uZFvly1j2enbaV9XAhtT9WtvL46XQzXf29cV/jbM00X0ET1HUnieQClVEjN82JHhhIexm6HDZ9D3i4Y9KdGr8ZqUbw5sRctowJ5Z8FOsvJLSYr0vPH6pL15D6017yzIoFNCKO/f0Bd1tvOftRsJg+6HeX+DKVfDpe8ae1duqr69+LoqpdYDW4GtSqm1Sqku9fjcaKXUdqVUhlLqiTMsd4VSSiul+tY/unAbS/8D0+6HoGjoPP6sVzexf0sA/v1bOoXlVWe9PlfT2PYm3E/GoWIy88q4/pxWTTc48sB7jUPo6bNh/ktNs06T1PcQ33vAQ1rrVlrrVsDDGNNRn5ZSygq8DVwEdAYmKqU6n2K5UOABYGVDggs3sv5zaDEA7l/bJN/mkiKDuLZ/S75fl80V7yzzxE4TDW5vwj3N22YM5XVepyYcKcU3EC7+p9ERae0nsOZjY1gxN1TfAhWstZ5/7InWegEQXMdn+gMZWutdWutKjMEuT/X1+W/AK0B5PbMId5K70zi013UCBEY22Wr/fmlXnh3bmR2Hij1xvL7GtDfhZj5bvoeXZ22jR4sImkc4YBSIoY9AaALMeBCWvdn063eC+haoXUqpp5VSrWtuTwF1XdqfCGTWep5V89pxNVNct9Ba/1zvxMK9pP5k3He4sElXq5Ti+nNaERfqz/8W7aK4wqMGW2hMexNuxG7XvDkvg47xobw1sZdjNhLREh7cDO0ugMWvQsZco7OSG1G6HoGVUpHA88BgjJ5Fi4Hntdb5Z/jMBGC01vq2mueTgAFa6/tqnluAecBNWus9SqkFwCNa6zUnryshIUGHh4cffz5mzBjGjh17/HlxcTEhIe41w6Q7ZoaG5W6z82NaZv5IYWg71vU502zmjTd7TxVfbqsk1BeeGxRIdOCpv3M5+/c9ffp0ZsyYAUB6evperXXr+n62oe3NE9sHeHbu3QU2nl9ezu3d/Dg3seE9WhsiqGQfvdc9jo+tlJ1tbiCz5RWnXM4l24jW+rQ3IAB4EHgLuBPwPdPyJ312IDC71vMngSdrPQ8HjgB7am7lwH6g78nr6tOnjz6T+fPnn/F9V+SOmbVuQO7SfK2fj9J68uVaH81yWB673a5nbzmgO/x1pn7k6w2nXc7M3zewRtevzTSqvXli+9Dac3Pb7Xb97E9bdPITM3RecYVzQpXkav3FVVq/EKv1kte0Li/6wyKu2EbqOsT3KdAX2IzR2eGfDSiQq4H2SqlkpZQfcA0wrVZhLNBax2itW2ujcq4AxulT7EEJN7RnCdirYfBDx+d5cgSlFBd2SeDqfi34cUM2n6/YS2W13WHbc7CzaW/CTbz8yzY+WbaHIe1jiQz2c85Gg6KMEc+jko1rpH682y0O99VVoDprra/XWv8PY9DKofVdsda6GrgPmA2kAV9rrbcqpV5QSsn01Z4u4zfwC4Gkfk7Z3L0j2tEiKoinftzCSzPTnLJNB2h0exPuoaLaxuQVexnRMZY3r3XQuafTCU2Ae1bAeU9B2jT48ALYv965GRqorgJ1/CKTmoLTIFrrmVrrDlrrtlrrF2tee0ZrPe0Uyw6XvScPseU72DgV2p4HPs75hhgfFsCcPw/j6r4t+HzFXvYcKXHKdpvYWbU34fpW7MqjtNLGpIGtCAtw7LmnU1LKOKox7Amjh+03N0HxYefnqKe6ClQPpVRhza0I6H7ssVKq0BkBhZs5kgHf3gIBETD4Qadu2mJRPHRhB/x8LFzwn4X8d+FOp26/CUh782CF5VV8sHgXIf4+DGobY14QixVGPAnXfPH7AM5rPzEvzxmcsUBpra1a67CaW6jW2qfW4zBnhRRuZHvNnDS3z4PEPk7ffHxYAN/dPYjeLSN5ZdY2dhwsqvtDLkLam2e7b8p6Fu84wpV9kwjwtZodB1oNgtvmGofhZz5G7KGlLndBbwMmHRGiHnYtgJgODu0YUZeUZmG8e30fgnytPPT1RhbvcN1DGMI7HCoqZ1H6YW4fkswzY/4woI55kvrAVZ9BYCRdUv8B0xs/VqYjSIESTafoIOxeaFwYaLKoYD9eurwbGYeKuenj1ewtdK1vhsK7zEszRju5tFfi2Q8I29RC4+FP68hKvMQYlmzGQ1C43+xUgBQo0VQOpsIbvYyu5V0uMzsNAON7JrLiyZGEBfjwRVolh4pkNC3hfKv35PHanB0kxwTTuZmLHqn1C2Zn21ug0xhj3rZPx7nExIdSoETT2PglVJXCpB+ghXO6ltdHeJAvD13QgfR8O4Nfns/avXlmRxJexG7X/OnL9ZRUVvP46E6ut/dUi7b4GB0nbvrZGD/zn+1g/v+Zer2UFCjRNDLmQPIQo2u5i5k0sDVPDQggLsyfR7/dRHmVHO4TzrE5u4ADBeX8bXxXRndNMDtO/bQebHzRbDsSFr4MX1wJuxeZEkUKlDh7+XvhUCq0b9oBYZtSu0grL4zvwq7DJfR64Tcmr9hrdiThBWZvzcFqUQztEGt2lIZpMwyumQID74PsNTD5cmNEdCdfMyUFSpydgmyY/oDxuNMYc7PU4bxO8Xx2S3+6JYXz7E9bmLxiL0eKK8yOJTzUB4t38e7CnQxpH0OUs4Y0akoWC4x6Ef60ARK6wq9PwXvD4PB250Vw2paEZ/r1r7BrPpxzjzHOl4sb2iGWj2/qR6eEMJ7+cQsX/mcR23Pc51op4R6Kyqv4x+ztdE8M59mxbj4ZcmAE3DbPODdVWQxv94ep10G147/cSYESjWerhox50GsSjP4/s9PUW7C/D9PuO5cvbz8HH4ti7FtLmPjeCjLzSs2OJjzE8p25VFbbeeKiFJJjPGCuSYvFODd1+3zjsN+2GfC/YbBusmM369C1C8+WvQYqCqDd+WYnaTAfq4WBbaP5+s6BXNu/JVv2F3D1/5bzxtwd5MphP3GWZm89SKi/D31aNd0s0i4huq1x2G/cW6DtMO0+47qpfSsdsjkpUKLx0qaDxRfaDDc7SaO1jgnmuXFd+OK2AQT7+/Dv39K5/N1lLN5xmLJK6e0nGu6LtAq+W5fFxd2a4efjof/F9p4Edy+DHhON66Y+GgUzH4Udc5q0W7qH/vaEw818FJa/Zew9BUaYneasdU+K4LeHhvH9PYPIK6lk0oerGPXaIrZkF1Btc9v5pYSTFZRWMXdfNYPbxfDERZ3MjuNYVh+47L/w+F7oeR2seh++uAI+uQQW/RPKz358YylQouFK84w/xjbDYdybZqdpUr1bRrLksfN457relFRUM+bNJQx4aS5frd4n10+JOi1IP4Rdw58v6OC8yQjNFhgBl74Nf9kPo1+BksMw70V4vTt8OREyVxnnqxtBCpRouN2LAA3D/wIhbnZ9Rz2EB/lycbdm/PLAEF66rBvJMcE8/t1muj/3K7d+spq5aQcpLK+qe0XCq9jtmp827CfMD3q2iDA7jvP5BcE5d8F9q+G2OdBhNGStNiZG3Px1o1bp08QRhTfYORf8w0yZTsOZ4sICuHZAS67p14KFOw6zLOMIX6/JYu62Q/hZLUzom8SwDrGc0yaa8EATJp8TLuXpn7Ywb9shLmzlg9XiukMaOUVSX+NWlg+pP0HLcxq1GilQokECS7Ngyw/Q/gLjGLQXsFgUIzrGMaJjHA9f2JENmUf5dm0WP6zLZsrKfTxxUSfuGtbW7JjCRMUV1Xy7NovhHWO5qpVbzubsGIGR0OemRn/cO/6HEU2joog+ax8FWyn0ut7sNKYI8LVyTptozmkTzYuXdWXDvqO0iAoyO5Yw2W+pOVRU27lvRDuK92wyO47HkHNQov4y5uJjK4WrJkO7kWanMZ2/j5UBbaJpHhFodhRhorJKG58t30tiRCC9W3rYdU8mkwIl6m/7TKp8QqHjxWYnEcJl3PPFWtbvO8p157TE4u3nnpqYFChRP7ZqSJ9NbnQ/rzn3JERdDhSUsSD9MBP7t+BuOQ/Z5KRAibqVF8CUK6H8KIdjB5qdRgiX8fOmA2gNdwxt69KTEbor+Sos6rZhCuycB31uIjfEs7uWC1FfBwrK+HzFXrolhnvGgLAuSPagRN1Sp0FcFxj7Oiir2WmEMF1ltZ3xby1lT24ptw52/Wlm3JUUKHFmRQdh33LoPM7sJEK4jOW7cjlUVMFrV/fk0l6JZsfxWFKgxOmVHYU5zwEaUqRACXHMnNSDBPhaGN01wewoHk3OQYnTm/4ApP4IKWMhvrPZaYRwCRsyjzJ9036GtI8lwFcOeTuSFChxauWFsO1n6HcbXPwvs9MI4RLySyq55r3l2DXccq6ce3I0KVDi1Hb8CvYq6DoBpPusEADM336I8io7398zSEaNcAI5ByX+6MgOWPEuBMdBi/5mpxHCZcxNO0RsqD89kyLMjuIVpECJE1WVwydjIHsNDH4QLHKMXQiA31IP8lvaQUZ2ipMhjZxEDvGJE6VNh+IcmDgVOl5kdhohXMK+3FLunLyGqGB/bpHrnpxGCpQ40dYfILQ5tB9ldhIhXMavqTnYNfxwzyCZXsWJ5BCfMNhtsOQ12DHbuCjXIn8aQhwzJ+0gHeJDpDg5mUP/F1JKjVZKbVdKZSilnjjF+w8ppVKVUpuUUnOVUq0cmUecwaavYc6zEN8Vzn3A7DRCuIxPl+1hxa48zk+JNzuK13FYgVJKWYG3gYuAzsBEpdTJV3uuB/pqrbsD3wL/cFQeUYdNUyEyGe5YAGHNzU4jhEvYmHmUZ6dtpVNCKDcOam12HK/jyD2o/kCG1nqX1roSmAqMr72A1nq+1rq05ukKIMmBecTpHMmA3Yugm1zzJERtv6bmYLUovrpjIPFhAWbH8TpKa+2YFSs1ARittb6t5vkkYIDW+r7TLP8WkKO1/vvJ7yUkJOjw8PDjz8eMGcPYsWOPPy8uLiYkJKSJfwLHcpXMsYcWk5L2GqBYcc7/qPSPPuPyrpK7oZyde/r06cyYMQOA9PT0vVrr1o7alie2D3CN3H9ZUkqYn+KJ/oH1/owr5G4Ml2wjWmuH3IAJwAe1nk8C3jrNstdj7EH5n+r9Pn366DOZP3/+Gd93RS6R2W7X+vVeWr/ZV+vM1fX6iEvkbgQzcwNrtIPamfbQ9qG1ubmrqm36ka836FaPz9AfL9nVoM/K77vhTtdGHNnNPBtoUet5Us1rJ1BKnQ/8FRimta5wYB5xsn3LIW8njH8HkvqanUYIlzEn7RDfrM1iTPdmXNO/pdlxvJYjz0GtBtorpZKVUn7ANcC02gsopXoB/wPGaa0POTCLONnWH+C72yEoGjqPr3t5IbzIr6k5hAf68trVPWXEchM5rEBprauB+4DZQBrwtdZ6q1LqBaXUscmF/gmEAN8opTYopaadZnWiKZXmwfd3GsMYjX8H/N3veLkQjlJQWsXsLTmMTInDxyrXA5rJoSNJaK1nAjNPeu2ZWo/Pd+T2xWlsmAK2CrhmCiR0NTuNEC7jUGE5o19fTEmljct7Sadis8lQR95Ea1j3KSz8B7Q6V4qTECf5fn02eSWVvDepD4Pbx5gdx+vJ/qs3yZhjzJIbFAkXvWJ2GiFczqwtOXRNDOPCLjKVuyuQAuVNVrwLIQlw72pI6GZ2GiFchtaaDxbvYkPmUcZ0l5FUXIUUKG9QUQS/PA4750L/28HHz+xEQriUX1MP8vef02gXF8KEPnLuyVXIOShv8OvTsPZjaDsSzrnb7DRCuJyfNmQTE+LP7AeHYpXJCF2G7EF5upIjsPFL6H0DTPoe/ILNTiSES9l/tIw5aYe4uFuCFCcXIwXKk+1bCZMvA1slDDzlEIhCeLXNWQWMfHUh1TY7E2XECJcjh/g8VWUpTJ0I2g6jXoLYjmYnEsLlfLR0NxYF3909iJRmYWbHESeRAuWp1n0Gpblw8yxoNdDsNEK4nLySSn7ZcoDLeiXRq2Wk2XHEKUiB8jS2apj+J2O0iNZDpDgJcQobM48y6cOVlFfZuf4cObTnquQclKdJ/RE2fGEMAHvpO2anEcIlvTU/A4DJt/anS/PwOpYWZpE9KE9SdhQW/RNiOsCEj8Ei3z+EONm6ffnMTTvIncPaMqR9rNlxxBlIgfIU+1bAp+OMQWCv/FSKkxCn8NOGbB6YuoFgPys3DWptdhxRBylQnmLBy8a0GVdMhbbnmZ1GCJdjt2vempdB+7gQPrmlP/FhAWZHEnWQr9nurroS1n4Cu+Yb1zpJcRLiDwrLq3ji+03sOFTMPSPakhgRaHYkUQ+yB+Xufn4I1k+GyNYw4E6z0wjhkv45aztfr8nikm7NGNcj0ew4op5kD8qdFR4whjHqcS3cvVyGMRLiFNbvy+er1ZlM7N+St6/rLcMZuREpUO7qwCb49hbj8bDHwC/I3DxCuKCv12Ry2TvL8Pex8MDI9mbHEQ0kh/jcUdlR+GQMVJXAeU9DVLLZiYRwOUXlVfxj1na6JYbz3g19SAiXThHuRgqUO1r1PlQUwJ2LoFkPs9MI4XIWph/m/inrKK6o5sMb+9IsXDpFuCMpUO6kogh+uAu2zYBOY6Q4CXEKheVVPPbtRiKD/Xj9ml70aBFhdiTRSFKg3MnS143i1Ocm49CeEOIEP67P5snvN1Nps/PtXQPp1TKSgooCgnyD2HJkC3FBcRwsOUhUQBQ+Fh8CfALwsxozTNvtdipsFfhafTlacZT4oHiOlB2hRWgLiiqLCPcPR2uNUtLJwlmkQLmDylJY/rZRoDpfCmNfNzuREC7FZtcsTD/Ek99vpnW85sGRKfyQ+R9mZPsyfdd0bHYblfbKP3zOR/kcLzhWZaXcVn78PYVCown3D6esqowuMV3YW7iXQc0HsfbgWgY1H8Sag2sY13YcW49s5Y7ud3Cg5ABaa6f93J5OCpQ7+Plh2DgFmveGi14xO40QLkVrzT1fLmF+ztcEN6sgO3gFj6z6Y5FICE6g2l5NuF84h0oPUW4rp8peBTWLVlF14npr3iioKABg/aH1AMzYNQOA73Z8B8Cb698EYF7mPACirFG889M7PNr3UUL9Qukc3RmrxdrEP7V3kALlysoLjVEiNk6Bcx+AC14wO5EQLuXbLUt5ec2zlNsL8I8ppxpIDEkkOiCaKzteSbBvMN1iuhHhH4Gf1Q+FQilFlb0KhSKvPA+tNZW2SipsFUQFRlFUWURMYAx55XlE+keyv2Q/kf6RpOam0i6yHSsPrOScZuew4sAK+if0Z0HmAjpGdeSb9G8orSplUdYi8o7mcecc48L5Ac0G0DqsNRM7TaRtRFtTf1/uRgqUq7LbYMrVsG8ZNOsJQx8zO5EQLmPrka3c8evdFFblg/Yjwi+Op899kJigGLrFdDt+Xul0fC2+AMQFxf3hvaiAKACCfY0L3zv4dQBgWNAwAC5vf/kJ99d3vh6Afgn9AJg1bxa9BvTivU3vkVuey9x9c1l5YCU/ZfxEZEAkd3S/g/Htxh/PIE5PCpQryt0JS/5tFKeL/gn9bpPRyYUA1h5cy9NLnyGrKBu7XWEp685/L3maQa06mR3tuABLAPHB8Tw90OjItCN/B5W2Sl5Y8QI5JTk8v/x5nl/+PFd2uJInBzwpheoMpEC5mtyd8L9hUFlk9NbrfztIryHh5Q6XHubVta/yy+5ZYAuisrgTV7e5h3uH9CfOxUclbx9pjGDx1ZivKKsu4411b7CncA/fpH/DN+nfcEX7K3j6nKflPNUpSIFyFdWVsPwtWPsxWKzG2Hrxnc1OdVa01mQXZxMfHM+GQxsI8gmiqKqIbbnbiA2KZUn2EtpFtGPj4Y2E+YVRZa+ioKKA5PBktudvp2t0V9Lz04kLisOmbVTYKugc3ZkDxQc4v9X5FFUW0T22+ykP0wjPsL94P9/v+J6fMmZwuPQw1aUtia64llv6DeCmc91vBJVAn0Ae7/84ANN3TmfO3jl8t+M7ftr5Ezd3uZn7e90v3dhrkQLlCkpyYdYTsPlriE2BS//rVsWpvLocq8XKjJ0zOFR6iH1F+1iTs4Zw/3DS8tLwt/pTYav4w+esyopN2wj1C6XKVoVSimDfYJbuX0qL0BaszllNQnACaw+uxaIsWC1WZu+ZjUIxdftUAHwsPoT4hhAXFMfQpKFYlIVRrUfhb/WnRWgLLEoOjbqjzMJMZhydwfqZGzhUdhBsgZRn38LQFv35z809CQtw/8NiY9uOZWzbsXy/43t+zPiR9ze/z08ZP3FR8kX8uc+fZY8KKVDmslXB7L/AqveM5+c9BUMfNTdTPWXkZzBz90yq7FVMSZtClb3qeLdci7LQPaY7h8sOc2PnGzlQcoDusd0J9AnE1+JLn/g+7C/ZT5+4PuSU5BAfHE+1vRqLsuBj8aGkqoRw/3AKKgoI8wujwlaB1WKl2l7NkbIjhPmFsShrEVEBUfy29zfKqstIz0/ng80fAPDeJuP32Sa8DcnhyfSM7UlIVQilVaUE+cqguq5sT8Eepu+azjfbvyO/IhdVHUFJ5n20CG7Hx3cNoE1siNkRm9zl7S/nsnaX8cHmD1i2fxmfpn7K1+lfMzhxMH8d8FeiAqK8dq9KCpSzaQ0FWbTN+ACWToSqUuNcU/erodUgs9Odktaa+ZnzyS3PZXLOZB6a/BA2bcOu7QAMbDaQpNAkusV0Y2DzgWitaRbS7IzrbBnWEoAWYS0ATuh1Fe4ffsJ9gI9xjsHX4kuLUGP5sW3HAnBu4rnHM1bYKiioKGBJ9hLKqsuYuXsm6fnpzN03F4B/fP0PLk6+mOYhzRnQbADdYrrJHpaL2F+8nx8yfuCL1C8pqiqAqhhK9v2ZpNBWfHRTL3q3jMTiwdNkKKW4vfvt3N79dqbtnMbS7KX8uudXftv7G+0j2/OX/n8hJTrleM9CbyEFylnydsOexcZArzmbSMIC3a4wClP7C8xOdwKtNVlFWWzN3cqX274kvyKf3QW7AQi0BDKu7Tj8rf7c0vUWNJqE4ASTExsNPMAngACfAK7ocAXwe/ffjYc3MmPFDI6GHWXm7pmUVZfx5vo3sSgLzYKb0TuuN7FBsfSJ70NyWDIJIQnSs8pJ0vPTmb5zOt9s/56S6kLsFXGUZj1Ml9i2XN6nkpvGnufRhelUxrUdx7i247g25VqWZi/ly21fcvPsmwnzC+O+XvfRO643HaM6mh3TKaRAOUp1JWStgoy5sHcZZK4wXo/pCBf8jVUFMQy4+FpzM9bIL88nvzyfxdmL2ZG/g/T8dNLy0gBoGdqS2KBYrut0Hf0S+rF93XYuGnSRyYkbpkdsD/JD8xk+bDgAhZWFLMhcwN7CvaTnp7P64Gpyy3L5aMtHgHEiu1VYKxJDEukS3YWYwBiSQpOIDogmOjCaML8wrz3kcrbs2k5uWS5z9s7ht73zWXtwNVpDdXkzfPPuYlS7bjx4ZQeSIoNYsGCB1xWn2nrE9qBHbA8mdJjAqpxVfLr1U15a+RIAF7a6kJToFMa0GeMSXxAdxaEFSik1GngdsAIfaK1fPul9f+AzoA+QC1yttd7jyExNzm4z9o6O7oXsdZC/xyhGuRnG+xYfiO0E5z8HbUdCfFewWChbsMC5MbUxEOaugl3sPLqTgyUHWXtoLQXlBWzN3Xr8/FFcUByxgbE82vdRkkKTGJI4BF/r73sT+yz7nJrbEcL8whjXdtwJr5VXl7P5yGayi7NJy01jf/H+Ew4P1hboE0hMYAzxQfFEB0ZzabtLGZw42Fnx3UpRZRFZRVmsOrCGjYdSWZ2zhqNVOQDYKuKwlfWklbqKy7p34LYhyfha5ZDryeKC4hjTZgyjW49md8Fuvt/xPTN3z+TXvb/y+rrXiQqIol9CP3rF9WJQ80G0DG3pMR0sHFaglFJW4G3gAiALWK2Umqa1Tq212K1Avta6nVLqGuAV4GpHZWoUuw3K8qE0F0qOGIWn6AAcSoMjO4zCVFlcs7CC4Fho3gu6XQlxKdD2PPAPdUg0rTWV9krKqsoorS7lUOkh8svzOVpxlJySHIqqithbuJfCikIyjmZQXFV8wufbR7Yn0j+Su3vcTcuwlnSM7Ei7yHYOyerqAnwC6JfQj37049J2lx5/vay6jLzyPPYV7iO/PJ/DZYfJKcnhSNkRDpUeYnvedvLK88wLbrIqexXFlcXkluWxOz+HtCO72VeYzZ7CnRwpzyGvah8aGwD26lDsFfGo0vG0j2zPmE6DGdw+lpRmYSb/FO7Bx+JD+8j2PN7/cR7v/zjZxdnM3DWTPYV7WL5/ObP3zAaM3rHdY7vTKqwVnaI6kRyWTLOQZjQLbnb8fK67cOQeVH8gQ2u9C0ApNRUYD9QuUOOB52oefwu8pZRSup7DARcXHWDl5s/Yd2AvtqXL0doO2l4zmrBGa13zWs09dkD//lyDtlVAVTm6ugxdVQaVxeiKYqPoVBZDVemxsSRPuNdB0RDWHOKHQkQLdGCUcV/zB6DRaCph76w/jG6stSa9KJ2cbTm/L6s1VfYqSqtLjxecsuoyyqrLKK0yHh97rfbzYx0VTiXAGkCrsFaE+Ycd7xyQEJxAl+guRPhHEBEQUZ9fs1cL9AkkMSSRxJBEs6M0ys7cHH7Yv57Viw9j03bs2n78Xp/0/NhjY+TvcqrsZZTZyiivLqPcVkaFrYxKexmV9nIqdQnVuhibKv3DNrVW6KpI7BVxBDKM5oEd6Bjdnn7NO9E9KYKUZqFyiLQJJIYkcnv32wHj/5QDJQdYkr2ErOIs1uasZUn2En7M+PGEzwT5BBETGEOEfwSh/qGE+YUR5BOEv9WfQ/mHSN2Qir/VnwCfAKzK2As7Nn4hGOd6tdbYtA2b3Wbc13SYqrZXH3+92l5Nlb2Kans11bqaK9pfQdeYrg3+GZWjhoZXSk0ARmutb6t5PgkYoLW+r9YyW2qWyap5vrNmmSO115WQkKDDw8OPPx8zZgxjx44l/+ganin41CH5zeSrfPFTfvgrf/wsNffKD3/L7/f+yv/E9y1+hFpCCbOGEWgJJMon6vgfWFMqLi4mJMT9uvo6O/f06dOZMcMY9To9PX2v1rq1o7Z1uvYB8OvBNKaXv9Oo9WqtwO6HtvuD3Q+l/VDaH4v2xaqDsOog/Akh2CeQMGsIUb5hxPtFkhQQQ1SAlXB/he9ZnEOSv7Wzl1edR151HrnVuRTYCii0FVJkK6LUXnr8VqkrqbJXUaWrqKb6rLdpwYJVWbFiNa5fxMrV0VfTI+jECVbr00bcopNEUlISa9as+cPr5WU9SNnXga1p2+japasxJJCygLKglAWUQikrClXrtd/vURbw8UP5BIFFnfhNgRPvf7/7/ZvEycuc/K3wlOureb5s2TIGDRp0wut+Fj8CfQJd+vjxggULGD58uNkxGszZuYcPH86rr74KgFLqSB2Ln5XTtQ+AlOJeRPwaRLeuXfFRVqwWCxalsFp8sCoLVovl+P2x930sPgT6BhBgDcDXaiHQz4qf1eL0vR75W3OuBQsWMHTYUCpsFVRUV2DTtuPnprXWxx+DcbjRqqxYlfX49YvHHtf376Q+bcSRBSobaFHreVLNa6daJksp5QOEY3SWqJeAwEg6dRxHzoEwOrYffpZxnSvMGkZMYIzZMYSHiw8Jp39UK4Z36GN2FOEGLMpCoE8ggT6BZkcBwJFdZlYD7ZVSyUopP+AaYNpJy0wDbqx5PAGYV9/zT0IIITybw/agtNbVSqn7gNkY3cw/0lpvVUq9AKzRWk8DPgQmK6UygDyMIiaEEEI49hyU1nomMPOk156p9bgcuNKRGYQQQrgnuSpOCCGES5ICJYQQwiV5RIGaPn262REazB0zg+R2R+76s0tu53LF3B5RoI5d7OVO3DEzSG535K4/u+R2LlfM7REFSgghhOdx2FBHTUkpdRjYe4ZFYgCHXq3vAO6YGSR3Y7TSWsc6auUe2j5Acjuby7URtyhQQgghvI8c4hNCCOGSpEAJIYRwSVKghBBCuCS3LlBKqdFKqe1KqQyl1BNm56kPpVQLpdR8pVSqUmqrUuoBszPVl1LKqpRar5Ryvf6op6GUilBKfauU2qaUSlNKDTQ7kzNJG3EuaSNNy207SdRMKZ9OrSnlgYknTSnvcpRSzYBmWut1SqlQYC1wqavnBlBKPQT0BcK01mPMzlMfSqlPgcVa6w9qRtUP0lofNTmWU0gbcT5pI03Lnfegjk8pr7WuBI5NKe/StNYHtNbrah4XAWmAy88nrpRKAi4BPjA7S30ppcKBoRij5qO1rnSVhuck0kacSNpI03PnApUIZNZ6noUb/BHXppRqDfQCVpocpT5eAx4D7CbnaIhk4DDwcc1hlw+UUsFmh3IiaSPO9RrSRpqUOxcot6aUCgG+Ax7UWheanedMlFJjgENa67VmZ2kgH6A38K7WuhdQArjFeRghbcRJXLqNuHOBqs+U8i5JKeWL0fC+0Fp/b3aeejgXGKeU2oNxmOg8pdTn5kaqlywgS2t97Nv3txiN0VtIG3EeaSMO4M4Fqj5TyrscpZTCON6bprX+t9l56kNr/aTWOklr3Rrj9zxPa329ybHqpLXOATKVUh1rXhoJuPyJ9iYkbcRJpI04hkNn1HWk000pb3Ks+jgXmARsVkptqHntLzWzD4umdz/wRc1/0LuAm03O4zTSRkQ9uWwbcdtu5kIIITybOx/iE0II4cGkQAkhhHBJUqCEEEK4JClQQgghXJIUKCGEEC5JCpQQQgiXJAXKDSilopVSG2puOUqp7JrHxUqpdxywvU+UUruVUnc18vPza7L1bepsQpyKtBHP5LYX6noTrXUu0BNAKfUcUKy1/peDN/uo1vrbxnxQaz1CKbWgifMIcVrSRjyT7EG5MaXU8GMToymlnlNKfaqUWqyU2quUulwp9Q+l1Gal1Kyasc1QSvVRSi1USq1VSs2umXunru18opR6Qym1TCm1Syk1oeb1ZkqpRTXfVLcopYY49icWomGkjbg3KVCepS1wHjAO+ByYr7XuBpQBl9Q0wDeBCVrrPsBHwIv1XHczYDAwBni55rVrgdla655AD2BD0/wYQjiMtBE3Iof4PMsvWusqpdRmjLHXZtW8vhloDXQEugK/GeNxYgUO1HPdP2qt7UCqUiq+5rXVwEc1jfpHrfWGJvkphHAcaSNuRPagPEsFQE0jqdK/D7Rox/gyooCtWuueNbduWusLG7LuGqpmO4swZuPMBj5RSt3QFD+EEA4kbcSNSIHyLtuBWKXUQDDm3FFKdWnsypRSrYCDWuv3Maa5dpl5ZIRoJGkjLkQO8XkRrXVlzcnbN5RS4Rj//q8BjZ2CYTjwqFKqCigG5NuhcGvSRlyLTLch/kAp9Qkwo7FdaGvWsQB4RGu9pqlyCeEqpI04hxziE6dSAPztbC5CBNoAVU2aSgjXIW3ECWQPSgghhEuSPSghhBAuSQqUEEIIlyQFSgghhEuSAiWEEMIl/T9H0ANj0uJc4gAAAABJRU5ErkJggg==\n", 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\n" }, "metadata": { "needs_background": "light" @@ -580,7 +615,7 @@ } ], "source": [ - "def plot_dynamics(exp, psi_init, seq, goal=-1):\n", + "def plot_dynamics(exp, psi_init, seq):\n", " \"\"\"\n", " Plotting code for time-resolved populations.\n", "\n", @@ -590,10 +625,6 @@ " Initial state or density matrix.\n", " seq: list\n", " List of operations to apply to the initial state.\n", - " goal: tf.float64\n", - " Value of the goal function, if used.\n", - " debug: boolean\n", - " If true, return a matplotlib figure instead of saving.\n", " \"\"\"\n", " model = exp.pmap.model\n", " dUs = exp.partial_propagators\n", @@ -652,8 +683,7 @@ "def plotSplittedPopulation(\n", " exp: Exp,\n", " psi_init: tf.Tensor,\n", - " sequence: List[str],\n", - " filename: str = None\n", + " sequence: List[str]\n", ") -> None:\n", " \"\"\"\n", " Plots time dependent populations for multiple qubits in separate plots.\n", @@ -685,24 +715,23 @@ " ts = np.linspace(0.0, dt * pop_t.shape[1], pop_t.shape[1])\n", "\n", " # create both subplots\n", + " titles = list(exp.pmap.model.subsystems.keys())\n", " fig, axs = plt.subplots(1, len(splitted), sharey=\"all\")\n", " for idx, ax in enumerate(axs):\n", " ax.plot(ts / 1e-9, splitted[idx].T)\n", " ax.tick_params(direction=\"in\", left=True, right=True, top=False, bottom=True)\n", " ax.set_xlabel(\"Time [ns]\")\n", " ax.set_ylabel(\"Population\")\n", + " ax.set_title(titles[idx])\n", " ax.legend([str(x) for x in np.arange(dims[idx])])\n", " ax.grid()\n", "\n", " plt.tight_layout()\n", - " if filename:\n", - " plt.savefig(filename)\n", " plt.show()\n", "\n", "sequence = [cnot12.get_key()]\n", "plot_dynamics(exp, init_state, sequence)\n", - "#plot_dynamics(exp, init_state, sequence * 10)\n", - "plotSplittedPopulation(exp, init_state, sequence, filename=\"dynamics_before.png\")" + "plotSplittedPopulation(exp, init_state, sequence)" ] }, { @@ -723,12 +752,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "cnot12[0, 1]-d1-gauss1-amp : 500.000 mV \n", + "cnot12[0, 1]-d1-gauss1-amp : 800.000 mV \n", "cnot12[0, 1]-d1-gauss1-freq_offset : -53.000 MHz 2pi \n", "cnot12[0, 1]-d1-gauss1-xy_angle : -444.089 arad \n", "cnot12[0, 1]-d1-gauss1-delta : -1.000 \n", - "cnot12[0, 1]-d1-carrier-framechange : 4.084 rad \n", - "cnot12[0, 1]-d2-gauss2-amp : 500.000 mV \n", + "cnot12[0, 1]-d1-carrier-framechange : 4.712 rad \n", + "cnot12[0, 1]-d2-gauss2-amp : 30.000 mV \n", "cnot12[0, 1]-d2-gauss2-freq_offset : -53.000 MHz 2pi \n", "cnot12[0, 1]-d2-gauss2-xy_angle : -444.089 arad \n", "cnot12[0, 1]-d2-gauss2-delta : -1.000 \n", @@ -764,7 +793,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As a fidelity function we choose average fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library." + "As a fidelity function we choose unitary fidelity as well as LBFG-S (a wrapper of the scipy implementation) from our library." ] }, { @@ -778,16 +807,14 @@ "from c3.optimizers.optimalcontrol import OptimalControl\n", "\n", "log_dir = os.path.join(tempfile.TemporaryDirectory().name, \"c3logs\")\n", - "# increase maxfevals to 500+ for better results\n", "opt = OptimalControl(\n", " dir_path=log_dir,\n", - " fid_func=fidelities.average_infid_set,\n", + " fid_func=fidelities.unitary_infid_set,\n", " fid_subspace=[\"Q1\", \"Q2\"],\n", " pmap=parameter_map,\n", - " algorithm=algorithms.cmaes,\n", + " algorithm=algorithms.lbfgs,\n", " options={\n", - " \"popsize\": 15,\n", - " \"maxfevals\": 100\n", + " \"maxfun\": 25\n", " },\n", " run_name=\"cnot12\"\n", ")" @@ -809,22 +836,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "C3:STATUS:Saving as: /tmp/tmpskwo3xlm/c3logs/cnot12/2021_12_06_T_22_24_59/open_loop.log\n", - "(7_w,15)-aCMA-ES (mu_w=4.5,w_1=34%) in dimension 10 (seed=964937, Mon Dec 6 22:24:59 2021)\n", - "Iterat #Fevals function value axis ratio sigma min&max std t[m:s]\n", - " 1 15 7.326344256215179e-01 1.0e+00 8.85e-02 8e-02 9e-02 0:13.8\n", - " 2 30 7.367559004446577e-01 1.2e+00 8.75e-02 8e-02 1e-01 0:27.4\n", - " 3 45 7.139157246650509e-01 1.3e+00 9.55e-02 9e-02 1e-01 0:40.8\n", - " 4 60 6.952586691625754e-01 1.5e+00 1.04e-01 9e-02 1e-01 0:54.5\n", - " 5 75 6.691554889600820e-01 1.8e+00 1.13e-01 1e-01 2e-01 1:08.0\n", - " 6 90 6.642503941891256e-01 1.9e+00 1.22e-01 1e-01 2e-01 1:21.7\n", - " 7 105 6.754441193654891e-01 1.9e+00 1.22e-01 1e-01 2e-01 1:35.7\n", - "termination on maxfevals=100\n", - "final/bestever f-value = 6.754441e-01 6.642504e-01\n", - "incumbent solution: [ 0.29354993 0.43400975 -0.62646119 0.04613593 -0.31716057 0.26852775\n", - " 0.54834962 -0.64817835 ...]\n", - "std deviations: [0.12406951 0.11915925 0.1206521 0.11160312 0.16109297 0.13345903\n", - " 0.11291629 0.10293621 ...]\n" + "C3:STATUS:Saving as: /tmp/tmpjx66lyg2/c3logs/cnot12/2021_12_08_T_12_27_05/open_loop.log\n", + "1 0.8790556354859858\n", + "2 0.9673489008768812\n", + "3 0.758622722337525\n", + "4 0.7679637459613755\n", + "5 0.6962301452070802\n", + "6 0.541321232138175\n", + "7 0.5682335581707882\n", + "8 0.382921410272719\n", + "9 0.43114251105289114\n", + "10 0.30099424375388173\n", + "11 0.32449492775751976\n", + "12 0.26537726105532744\n", + "13 0.2653362073570743\n", + "14 0.25121669688810866\n", + "15 0.23925168937407626\n", + "16 0.18551042816386099\n", + "17 0.1305543307431979\n", + "18 0.07413739981051659\n", + "19 0.031551815290153495\n", + "20 0.017447484467834062\n", + "21 0.007924221221055072\n", + "22 0.006483318391815374\n", + "23 0.005732979353259449\n", + "24 0.005594385264244273\n", + "25 0.0055582927728303755\n", + "26 0.005521343169743842\n" ] } ], @@ -850,18 +888,18 @@ "name": "stdout", "output_type": "stream", "text": [ - "cnot12[0, 1]-d1-gauss1-amp : 461.437 mV \n", - "cnot12[0, 1]-d1-gauss1-freq_offset : -52.704 MHz 2pi \n", - "cnot12[0, 1]-d1-gauss1-xy_angle : 355.730 mrad \n", - "cnot12[0, 1]-d1-gauss1-delta : -1.058 \n", - "cnot12[0, 1]-d1-carrier-framechange : 827.654 mrad \n", - "cnot12[0, 1]-d2-gauss2-amp : 428.475 mV \n", - "cnot12[0, 1]-d2-gauss2-freq_offset : -53.037 MHz 2pi \n", - "cnot12[0, 1]-d2-gauss2-xy_angle : 73.836 mrad \n", - "cnot12[0, 1]-d2-gauss2-delta : -484.207 m \n", - "cnot12[0, 1]-d2-carrier-framechange : 120.479 mrad \n", + "cnot12[0, 1]-d1-gauss1-amp : 2.359 V \n", + "cnot12[0, 1]-d1-gauss1-freq_offset : -53.252 MHz 2pi \n", + "cnot12[0, 1]-d1-gauss1-xy_angle : 587.818 mrad \n", + "cnot12[0, 1]-d1-gauss1-delta : -743.473 m \n", + "cnot12[0, 1]-d1-carrier-framechange : -815.216 mrad \n", + "cnot12[0, 1]-d2-gauss2-amp : 56.719 mV \n", + "cnot12[0, 1]-d2-gauss2-freq_offset : -53.176 MHz 2pi \n", + "cnot12[0, 1]-d2-gauss2-xy_angle : -135.515 mrad \n", + "cnot12[0, 1]-d2-gauss2-delta : -519.864 m \n", + "cnot12[0, 1]-d2-carrier-framechange : 598.919 mrad \n", "\n", - "0.6642503941891256\n" + "0.005521343169743842\n" ] } ], @@ -889,10 +927,8 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n" 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\n", 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\n" }, "metadata": { "needs_background": "light" @@ -914,8 +948,101 @@ ], "source": [ "plot_dynamics(exp, init_state, sequence)\n", - "plotSplittedPopulation(exp, init_state, sequence, filename=\"dynamics_after.png\")" + "plotSplittedPopulation(exp, init_state, sequence)" ] + }, + { + "cell_type": "markdown", + "source": [ + "Now we plot the dynamics for the control in the excited state." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": 23, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tf.Tensor(\n", + "[[0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [1.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]\n", + " [0.+0.j]], shape=(9, 1), dtype=complex128)\n" + ] + }, + { + "data": { + "text/plain": "
", + "image/png": 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "psi_init = [[0] * 9]\n", + "psi_init[0][4] = 1\n", + "init_state = tf.transpose(tf.constant(psi_init, tf.complex128))\n", + "print(init_state)\n", + "\n", + "plot_dynamics(exp, init_state, sequence)\n", + "plotSplittedPopulation(exp, init_state, sequence)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "As intended, the dynamics of the target is dependent on the control qubit performing a flip if the control is excited and an identity otherwise." + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%% md\n" + } + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } } ], "metadata": { @@ -942,4 +1069,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} +} \ No newline at end of file From 92dea6339802328c0d650b124ff796fb1653bb31 Mon Sep 17 00:00:00 2001 From: Alexander Simm Date: Wed, 8 Dec 2021 12:41:06 +0100 Subject: [PATCH 49/80] changelog --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 22311dfa..e3cad650 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -18,6 +18,7 @@ This Changelog tracks all past changes to this project as well as details about - `added` example for the log reader CLI #137 - `added` human readable saving of current best point for the optimizer #140 - `fixed` handling of anharmonicity in transmons with two levels #146 +- `added` an example notebook with entangling two-qubit gates #154 ## Version `1.3` - 20 Jul 2021 From 79de5976140d7ce71529d54b8113f3db95945b74 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Wed, 8 Dec 2021 12:54:11 +0100 Subject: [PATCH 50/80] add missing pandas dependency --- examples/Simulated_Model_Learning.ipynb | 1032 ++++++++++++----------- 1 file changed, 517 insertions(+), 515 deletions(-) diff --git a/examples/Simulated_Model_Learning.ipynb b/examples/Simulated_Model_Learning.ipynb index 479a8d28..61cbfbd6 100644 --- a/examples/Simulated_Model_Learning.ipynb +++ b/examples/Simulated_Model_Learning.ipynb @@ -2,29 +2,40 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Model Learning on Dataset from a Simulated Experiment" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "In this notebook, we will use a dataset from a simulated experiment, more specifically, the `Simulated_calibration.ipynb` example notebook and perform Model Learning on a simple 1 qubit model." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Imports" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:16.689570Z", + "iopub.status.busy": "2021-06-30T06:29:16.684069Z", + "iopub.status.idle": "2021-06-30T06:29:24.343696Z", + "shell.execute_reply": "2021-06-30T06:29:24.343985Z" + } + }, + "outputs": [], "source": [ + "!pip install pandas matplotlib\n", + "\n", "import pickle\n", "from pprint import pprint\n", "import copy\n", @@ -48,38 +59,25 @@ "import c3.libraries.tasks as tasks\n", "from c3.optimizers.modellearning import ModelLearning\n", "from c3.optimizers.sensitivity import Sensitivity" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:16.689570Z", - "iopub.status.busy": "2021-06-30T06:29:16.684069Z", - "iopub.status.idle": "2021-06-30T06:29:24.343696Z", - "shell.execute_reply": "2021-06-30T06:29:24.343985Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## The Dataset" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We first take a look below at the dataset and its properties. To explore more details about how the dataset is generated, please refer to the `Simulated_calibration.ipynb` example notebook." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 2, - "source": [ - "DATAFILE_PATH = \"data/small_dataset.pkl\"" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.346337Z", @@ -87,16 +85,15 @@ "iopub.status.idle": "2021-06-30T06:29:24.347420Z", "shell.execute_reply": "2021-06-30T06:29:24.347670Z" } - } + }, + "outputs": [], + "source": [ + "DATAFILE_PATH = \"data/small_dataset.pkl\"" + ] }, { "cell_type": "code", "execution_count": 3, - "source": [ - "with open(DATAFILE_PATH, \"rb+\") as file:\n", - " data = pickle.load(file)" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.349797Z", @@ -104,51 +101,60 @@ "iopub.status.idle": "2021-06-30T06:29:24.403393Z", "shell.execute_reply": "2021-06-30T06:29:24.403893Z" } - } + }, + "outputs": [], + "source": [ + "with open(DATAFILE_PATH, \"rb+\") as file:\n", + " data = pickle.load(file)" + ] }, { "cell_type": "code", "execution_count": 4, - "source": [ - "data.keys()" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.414664Z", + "iopub.status.busy": "2021-06-30T06:29:24.414128Z", + "iopub.status.idle": "2021-06-30T06:29:24.416895Z", + "shell.execute_reply": "2021-06-30T06:29:24.417303Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['seqs_grouped_by_param_set', 'opt_map'])" ] }, + "execution_count": 4, "metadata": {}, - "execution_count": 4 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.414664Z", - "iopub.status.busy": "2021-06-30T06:29:24.414128Z", - "iopub.status.idle": "2021-06-30T06:29:24.416895Z", - "shell.execute_reply": "2021-06-30T06:29:24.417303Z" - } - } + "source": [ + "data.keys()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Since this dataset was obtained from an ORBIT ([arXiv:1403.0035](https://arxiv.org/abs/1403.0035)) calibration experiment, we have the `opt_map` which will tell us about the gateset parameters being optimized." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 5, - "source": [ - "data[\"opt_map\"]" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.421146Z", + "iopub.status.busy": "2021-06-30T06:29:24.420618Z", + "iopub.status.idle": "2021-06-30T06:29:24.423293Z", + "shell.execute_reply": "2021-06-30T06:29:24.423687Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[['rx90p[0]-d1-gauss-amp',\n", @@ -166,42 +172,34 @@ " ['id[0]-d1-carrier-framechange']]" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.421146Z", - "iopub.status.busy": "2021-06-30T06:29:24.420618Z", - "iopub.status.idle": "2021-06-30T06:29:24.423293Z", - "shell.execute_reply": "2021-06-30T06:29:24.423687Z" - } - } + "source": [ + "data[\"opt_map\"]" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "This `opt_map` implies the calibration experiment focussed on optimizing \n", "the amplitude, delta and frequency offset of the gaussian pulse, along \n", "with the framechange angle" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Now onto the actual measurement data from the experiment runs" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 6, - "source": [ - "seqs_data = data[\"seqs_grouped_by_param_set\"]" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.426544Z", @@ -209,58 +207,58 @@ "iopub.status.idle": "2021-06-30T06:29:24.428263Z", "shell.execute_reply": "2021-06-30T06:29:24.427861Z" } - } + }, + "outputs": [], + "source": [ + "seqs_data = data[\"seqs_grouped_by_param_set\"]" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**How many experiment runs do we have?**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 7, - "source": [ - "len(seqs_data)" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.431169Z", + "iopub.status.busy": "2021-06-30T06:29:24.430725Z", + "iopub.status.idle": "2021-06-30T06:29:24.433094Z", + "shell.execute_reply": "2021-06-30T06:29:24.433444Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "41" ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.431169Z", - "iopub.status.busy": "2021-06-30T06:29:24.430725Z", - "iopub.status.idle": "2021-06-30T06:29:24.433094Z", - "shell.execute_reply": "2021-06-30T06:29:24.433444Z" - } - } + "source": [ + "len(seqs_data)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**What does the data from each experiment look like?**\n", "\n", "We take a look at the first data point" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 8, - "source": [ - "example_data_point = seqs_data[0]" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.436247Z", @@ -268,99 +266,107 @@ "iopub.status.idle": "2021-06-30T06:29:24.437588Z", "shell.execute_reply": "2021-06-30T06:29:24.437939Z" } - } + }, + "outputs": [], + "source": [ + "example_data_point = seqs_data[0]" + ] }, { "cell_type": "code", "execution_count": 9, - "source": [ - "example_data_point.keys()" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.440859Z", + "iopub.status.busy": "2021-06-30T06:29:24.440410Z", + "iopub.status.idle": "2021-06-30T06:29:24.443139Z", + "shell.execute_reply": "2021-06-30T06:29:24.442720Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['params', 'seqs', 'results', 'results_std', 'shots'])" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.440859Z", - "iopub.status.busy": "2021-06-30T06:29:24.440410Z", - "iopub.status.idle": "2021-06-30T06:29:24.443139Z", - "shell.execute_reply": "2021-06-30T06:29:24.442720Z" - } - } + "source": [ + "example_data_point.keys()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "These `keys` are useful in understanding the structure of the dataset. We look at them one by one." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 10, - "source": [ - "example_data_point[\"params\"]" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.447311Z", + "iopub.status.busy": "2021-06-30T06:29:24.446850Z", + "iopub.status.idle": "2021-06-30T06:29:24.450577Z", + "shell.execute_reply": "2021-06-30T06:29:24.450890Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[450.000 mV, -1.000 , -50.500 MHz 2pi, 4.084 rad]" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.447311Z", - "iopub.status.busy": "2021-06-30T06:29:24.446850Z", - "iopub.status.idle": "2021-06-30T06:29:24.450577Z", - "shell.execute_reply": "2021-06-30T06:29:24.450890Z" - } - } + "source": [ + "example_data_point[\"params\"]" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "These are the parameters for our parameterised gateset, for the first experiment run. They correspond to the optimization parameters we previously discussed. " - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The `seqs` key stores the sequence of gates that make up this ORBIT calibration experiment. Each ORBIT sequence consists of a set of gates, followed by a measurement operation. This is then repeated for some `n` number of shots (eg, `1000` in this case) and we only store the averaged result along with the standard deviation of these readout shots. Each experiment in turn consists of a number of these ORBIT sequences. The terms *sequence*, *set* and *experiment* are used somewhat loosely here, so we show below what these look like." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**A single ORBIT sequence**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 11, - "source": [ - "example_data_point[\"seqs\"][0]" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.454002Z", + "iopub.status.busy": "2021-06-30T06:29:24.453650Z", + "iopub.status.idle": "2021-06-30T06:29:24.455626Z", + "shell.execute_reply": "2021-06-30T06:29:24.455901Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "['ry90p[0]',\n", @@ -391,104 +397,93 @@ " 'rx90p[0]']" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.454002Z", - "iopub.status.busy": "2021-06-30T06:29:24.453650Z", - "iopub.status.idle": "2021-06-30T06:29:24.455626Z", - "shell.execute_reply": "2021-06-30T06:29:24.455901Z" - } - } + "source": [ + "example_data_point[\"seqs\"][0]" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**Total number of ORBIT sequences in an experiment**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 12, - "source": [ - "len(example_data_point[\"seqs\"])" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.458262Z", + "iopub.status.busy": "2021-06-30T06:29:24.457903Z", + "iopub.status.idle": "2021-06-30T06:29:24.459781Z", + "shell.execute_reply": "2021-06-30T06:29:24.460029Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "20" ] }, + "execution_count": 12, "metadata": {}, - "execution_count": 12 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.458262Z", - "iopub.status.busy": "2021-06-30T06:29:24.457903Z", - "iopub.status.idle": "2021-06-30T06:29:24.459781Z", - "shell.execute_reply": "2021-06-30T06:29:24.460029Z" - } - } + "source": [ + "len(example_data_point[\"seqs\"])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**Total number of Measurement results**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 13, - "source": [ - "len(example_data_point[\"results\"])" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.462194Z", + "iopub.status.busy": "2021-06-30T06:29:24.461850Z", + "iopub.status.idle": "2021-06-30T06:29:24.463711Z", + "shell.execute_reply": "2021-06-30T06:29:24.463958Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "20" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 13 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.462194Z", - "iopub.status.busy": "2021-06-30T06:29:24.461850Z", - "iopub.status.idle": "2021-06-30T06:29:24.463711Z", - "shell.execute_reply": "2021-06-30T06:29:24.463958Z" - } - } + "source": [ + "len(example_data_point[\"results\"])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**The measurement results and the standard deviation look like this**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 14, - "source": [ - "example_results = [\n", - " (example_data_point[\"results\"][i], example_data_point[\"results_std\"][i])\n", - " for i in range(len(example_data_point[\"results\"]))\n", - "]" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.466251Z", @@ -496,18 +491,30 @@ "iopub.status.idle": "2021-06-30T06:29:24.467336Z", "shell.execute_reply": "2021-06-30T06:29:24.467583Z" } - } + }, + "outputs": [], + "source": [ + "example_results = [\n", + " (example_data_point[\"results\"][i], example_data_point[\"results_std\"][i])\n", + " for i in range(len(example_data_point[\"results\"]))\n", + "]" + ] }, { "cell_type": "code", "execution_count": 15, - "source": [ - "pprint(example_results)" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.470224Z", + "iopub.status.busy": "2021-06-30T06:29:24.469909Z", + "iopub.status.idle": "2021-06-30T06:29:24.473051Z", + "shell.execute_reply": "2021-06-30T06:29:24.472782Z" + } + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "[([0.745], [0.013783141876945182]),\n", " ([0.213], [0.012947239087929134]),\n", @@ -532,39 +539,43 @@ ] } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.470224Z", - "iopub.status.busy": "2021-06-30T06:29:24.469909Z", - "iopub.status.idle": "2021-06-30T06:29:24.473051Z", - "shell.execute_reply": "2021-06-30T06:29:24.472782Z" - } - } + "source": [ + "pprint(example_results)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## The Model for Model Learning" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "An initial model needs to be provided, which we refine by fitting to our calibration data. We do this below. If you want to learn more about what the various components of the model mean, please refer back to the `two_qubits.ipynb` notebook or the documentation." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Define Constants" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.475726Z", + "iopub.status.busy": "2021-06-30T06:29:24.475401Z", + "iopub.status.idle": "2021-06-30T06:29:24.476822Z", + "shell.execute_reply": "2021-06-30T06:29:24.477068Z" + } + }, + "outputs": [], "source": [ "lindblad = False\n", "dressed = True\n", @@ -578,27 +589,27 @@ "awg_res = 2e9\n", "sideband = 50e6\n", "lo_freq = 5e9 + sideband" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.475726Z", - "iopub.status.busy": "2021-06-30T06:29:24.475401Z", - "iopub.status.idle": "2021-06-30T06:29:24.476822Z", - "shell.execute_reply": "2021-06-30T06:29:24.477068Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Model" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.480926Z", + "iopub.status.busy": "2021-06-30T06:29:24.480595Z", + "iopub.status.idle": "2021-06-30T06:29:24.488368Z", + "shell.execute_reply": "2021-06-30T06:29:24.488635Z" + } + }, + "outputs": [], "source": [ "q1 = chip.Qubit(\n", " name=\"Q1\",\n", @@ -636,27 +647,27 @@ "model = Mdl(phys_components, line_components, task_list)\n", "model.set_lindbladian(lindblad)\n", "model.set_dressed(dressed)" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.480926Z", - "iopub.status.busy": "2021-06-30T06:29:24.480595Z", - "iopub.status.idle": "2021-06-30T06:29:24.488368Z", - "shell.execute_reply": "2021-06-30T06:29:24.488635Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Generator" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.492657Z", + "iopub.status.busy": "2021-06-30T06:29:24.492317Z", + "iopub.status.idle": "2021-06-30T06:29:24.494580Z", + "shell.execute_reply": "2021-06-30T06:29:24.494836Z" + } + }, + "outputs": [], "source": [ "generator = Gnr(\n", " devices={\n", @@ -685,27 +696,27 @@ " },\n", ")\n", "generator.devices[\"AWG\"].enable_drag_2()" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.492657Z", - "iopub.status.busy": "2021-06-30T06:29:24.492317Z", - "iopub.status.idle": "2021-06-30T06:29:24.494580Z", - "shell.execute_reply": "2021-06-30T06:29:24.494836Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Gateset" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.501528Z", + "iopub.status.busy": "2021-06-30T06:29:24.501168Z", + "iopub.status.idle": "2021-06-30T06:29:24.511660Z", + "shell.execute_reply": "2021-06-30T06:29:24.511908Z" + } + }, + "outputs": [], "source": [ "gauss_params_single = {\n", " \"amp\": Qty(value=0.45, min_val=0.4, max_val=0.6, unit=\"V\"),\n", @@ -776,35 +787,18 @@ "ry90p.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(0.5 * np.pi)\n", "rx90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(np.pi)\n", "ry90m.comps[\"d1\"][\"gauss\"].params[\"xy_angle\"].set_value(1.5 * np.pi)" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.501528Z", - "iopub.status.busy": "2021-06-30T06:29:24.501168Z", - "iopub.status.idle": "2021-06-30T06:29:24.511660Z", - "shell.execute_reply": "2021-06-30T06:29:24.511908Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Experiment" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 20, - "source": [ - "parameter_map = PMap(\n", - " instructions=[QId, rx90p, ry90p, rx90m, ry90m], model=model, generator=generator\n", - ")\n", - "\n", - "exp = Exp(pmap=parameter_map)" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.514280Z", @@ -812,16 +806,19 @@ "iopub.status.idle": "2021-06-30T06:29:24.515429Z", "shell.execute_reply": "2021-06-30T06:29:24.515676Z" } - } + }, + "outputs": [], + "source": [ + "parameter_map = PMap(\n", + " instructions=[QId, rx90p, ry90p, rx90m, ry90m], model=model, generator=generator\n", + ")\n", + "\n", + "exp = Exp(pmap=parameter_map)" + ] }, { "cell_type": "code", "execution_count": 21, - "source": [ - "exp_opt_map = [[('Q1', 'anhar')], [('Q1', 'freq')]]\n", - "exp.pmap.set_opt_map(exp_opt_map)" - ], - "outputs": [], "metadata": { "execution": { "iopub.execute_input": "2021-06-30T06:29:24.517795Z", @@ -829,18 +826,32 @@ "iopub.status.idle": "2021-06-30T06:29:24.518939Z", "shell.execute_reply": "2021-06-30T06:29:24.519217Z" } - } + }, + "outputs": [], + "source": [ + "exp_opt_map = [[('Q1', 'anhar')], [('Q1', 'freq')]]\n", + "exp.pmap.set_opt_map(exp_opt_map)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Optimizer " - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.522171Z", + "iopub.status.busy": "2021-06-30T06:29:24.521841Z", + "iopub.status.idle": "2021-06-30T06:29:24.523305Z", + "shell.execute_reply": "2021-06-30T06:29:24.523566Z" + } + }, + "outputs": [], "source": [ "datafiles = {\"orbit\": DATAFILE_PATH} # path to the dataset\n", "run_name = \"simple_model_learning\" # name of the optimization run\n", @@ -870,20 +881,20 @@ " ],\n", " ]\n", "} # the excited states of the qubit model, in this case it is 3-level" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.522171Z", - "iopub.status.busy": "2021-06-30T06:29:24.521841Z", - "iopub.status.idle": "2021-06-30T06:29:24.523305Z", - "shell.execute_reply": "2021-06-30T06:29:24.523566Z" - } - } + ] }, { "cell_type": "code", "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.525945Z", + "iopub.status.busy": "2021-06-30T06:29:24.525624Z", + "iopub.status.idle": "2021-06-30T06:29:24.531653Z", + "shell.execute_reply": "2021-06-30T06:29:24.531905Z" + } + }, + "outputs": [], "source": [ "opt = ModelLearning(\n", " datafiles=datafiles,\n", @@ -898,41 +909,37 @@ ")\n", "\n", "opt.set_exp(exp)" - ], - "outputs": [], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.525945Z", - "iopub.status.busy": "2021-06-30T06:29:24.525624Z", - "iopub.status.idle": "2021-06-30T06:29:24.531653Z", - "shell.execute_reply": "2021-06-30T06:29:24.531905Z" - } - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Model Learning" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We are now ready to learn from the data and improve our model" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 24, - "source": [ - "opt.run()" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:29:24.534091Z", + "iopub.status.busy": "2021-06-30T06:29:24.533766Z", + "iopub.status.idle": "2021-06-30T06:33:52.900803Z", + "shell.execute_reply": "2021-06-30T06:33:52.900453Z" + } + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "C3:STATUS:Saving as: /home/users/anurag/c3/examples/ml_logs/simple_model_learning/2021_07_05_T_20_54_20/model_learn.log\n", "(6_w,12)-aCMA-ES (mu_w=3.7,w_1=40%) in dimension 2 (seed=612179, Mon Jul 5 20:54:20 2021)\n", @@ -948,59 +955,59 @@ ] } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:29:24.534091Z", - "iopub.status.busy": "2021-06-30T06:29:24.533766Z", - "iopub.status.idle": "2021-06-30T06:33:52.900803Z", - "shell.execute_reply": "2021-06-30T06:33:52.900453Z" - } - } + "source": [ + "opt.run()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Result of Model Learning" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 25, - "source": [ - "opt.current_best_goal" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:33:52.903299Z", + "iopub.status.busy": "2021-06-30T06:33:52.902952Z", + "iopub.status.idle": "2021-06-30T06:33:52.904734Z", + "shell.execute_reply": "2021-06-30T06:33:52.904981Z" + } + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "-0.031570491979296046" ] }, + "execution_count": 25, "metadata": {}, - "execution_count": 25 + "output_type": "execute_result" } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:33:52.903299Z", - "iopub.status.busy": "2021-06-30T06:33:52.902952Z", - "iopub.status.idle": "2021-06-30T06:33:52.904734Z", - "shell.execute_reply": "2021-06-30T06:33:52.904981Z" - } - } + "source": [ + "opt.current_best_goal" + ] }, { "cell_type": "code", "execution_count": 26, - "source": [ - "print(opt.pmap.str_parameters(opt.pmap.opt_map))" - ], + "metadata": { + "execution": { + "iopub.execute_input": "2021-06-30T06:33:52.907388Z", + "iopub.status.busy": "2021-06-30T06:33:52.907044Z", + "iopub.status.idle": "2021-06-30T06:33:52.908879Z", + "shell.execute_reply": "2021-06-30T06:33:52.909149Z" + } + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "Q1-anhar : -210.057 MHz 2pi \n", "Q1-freq : 5.000 GHz 2pi \n", @@ -1008,79 +1015,76 @@ ] } ], - "metadata": { - "execution": { - "iopub.execute_input": "2021-06-30T06:33:52.907388Z", - "iopub.status.busy": "2021-06-30T06:33:52.907044Z", - "iopub.status.idle": "2021-06-30T06:33:52.908879Z", - "shell.execute_reply": "2021-06-30T06:33:52.909149Z" - } - } + "source": [ + "print(opt.pmap.str_parameters(opt.pmap.opt_map))" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Visualisation & Analysis of Results" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The Model Learning logs provide a useful way to visualise the learning process and also understand what's going wrong (or right). We now process these logs to read some data points and also plot some visualisations of the Model Learning process" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Open, Clean-up and Convert Logfiles" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 27, + "metadata": {}, + "outputs": [], "source": [ "LOGDIR = opt.logdir" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 28, + "metadata": {}, + "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"model_learn.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 29, - "source": [ - "params_names = [\n", - " item for sublist in (ast.literal_eval(log[3].strip(\"\\n\"))) for item in sublist\n", - "]\n", - "print(params_names)" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "['Q1-anhar', 'Q1-freq']\n" ] } ], - "metadata": {} + "source": [ + "params_names = [\n", + " item for sublist in (ast.literal_eval(log[3].strip(\"\\n\"))) for item in sublist\n", + "]\n", + "print(params_names)" + ] }, { "cell_type": "code", "execution_count": 30, + "metadata": {}, + "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1090,35 +1094,30 @@ " temp_dict[param_name] = temp_dict[\"params\"][index]\n", " temp_dict.pop(\"params\")\n", " data_list_dict.append(temp_dict)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 31, + "metadata": {}, + "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Summary of Logs" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 32, - "source": [ - "data_df.describe()" - ], + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -1209,89 +1208,94 @@ "max 61.088377 -2.040499e+08 5.001544e+09" ] }, + "execution_count": 32, "metadata": {}, - "execution_count": 32 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "data_df.describe()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**Best Point**" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 33, + "metadata": {}, + "outputs": [], "source": [ "best_point_file = os.path.join(LOGDIR, 'best_point_model_learn.log')" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 34, - "source": [ - "with open(best_point_file, \"r\") as f:\n", - " best_point_log_dict = hjson.load(f)\n", - "\n", - "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n", - "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n", - "print(best_point_dict)" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "{'Q1-anhar': -210057285.86145476, 'Q1-freq': 5000081146.521889, 'goal': -0.031570491979296046}\n" ] } ], - "metadata": {} + "source": [ + "with open(best_point_file, \"r\") as f:\n", + " best_point_log_dict = hjson.load(f)\n", + "\n", + "best_point_dict = dict(zip(params_names, best_point_log_dict[\"optim_status\"][\"params\"]))\n", + "best_point_dict[\"goal\"] = best_point_log_dict[\"optim_status\"][\"goal\"]\n", + "print(best_point_dict)" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Plotting" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We use `matplotlib` to produce the plots below. Please make sure you have the same installed in your python environment." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 35, + "metadata": {}, + "outputs": [], "source": [ "!pip install -q matplotlib" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 36, + "metadata": {}, + "outputs": [], "source": [ "from matplotlib.ticker import MaxNLocator\n", "from matplotlib import rcParams\n", "from matplotlib import cycler\n", "import matplotlib as mpl\n", "import matplotlib.pyplot as plt " - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 37, + "metadata": {}, + "outputs": [], "source": [ "rcParams[\"axes.grid\"] = True\n", "rcParams[\"grid.linestyle\"] = \"--\"\n", @@ -1300,52 +1304,38 @@ "rcParams[\"text.usetex\"] = False\n", "rcParams[\"font.size\"] = 16\n", "rcParams[\"font.family\"] = \"serif\"" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "**In the plots below, the blue line shows the progress of the parameter optimization while the black and the red lines indicate the converged and true value respectively**" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Qubit Anharmonicity" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 38, - "source": [ - "plot_item = \"Q1-anhar\"\n", - "true_value = -210e6\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Iteration\")\n", - "ax.set_ylabel(plot_item)\n", - "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", - "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", - "ax.plot(data_df[plot_item])" - ], + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 38, "metadata": {}, - "execution_count": 38 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ @@ -1354,24 +1344,13 @@ }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "markdown", - "source": [ - "### Qubit Frequency" - ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 39, "source": [ - "plot_item = \"Q1-freq\"\n", - "true_value = 5e9\n", + "plot_item = \"Q1-anhar\"\n", + "true_value = -210e6\n", "\n", "fig = plt.figure(figsize=(12, 8))\n", "ax = fig.add_subplot(111)\n", @@ -1380,20 +1359,31 @@ "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", "ax.plot(data_df[plot_item])" - ], + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Qubit Frequency" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 39, "metadata": {}, - "execution_count": 39 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ @@ -1402,45 +1392,46 @@ }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "plot_item = \"Q1-freq\"\n", + "true_value = 5e9\n", + "\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Iteration\")\n", + "ax.set_ylabel(plot_item)\n", + "ax.axhline(y=true_value, color=\"red\", linestyle=\"--\")\n", + "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", + "ax.plot(data_df[plot_item])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### Goal Function" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 40, - "source": [ - "plot_item = \"goal\"\n", - "\n", - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Iteration\")\n", - "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", - "ax.set_ylabel(plot_item)\n", - "\n", - "ax.plot(data_df[plot_item])" - ], + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "[]" ] }, + "execution_count": 40, "metadata": {}, - "execution_count": 40 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ @@ -1449,28 +1440,41 @@ }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "plot_item = \"goal\"\n", + "\n", + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Iteration\")\n", + "ax.axhline(y=best_point_dict[plot_item], color=\"black\", linestyle=\"-.\")\n", + "ax.set_ylabel(plot_item)\n", + "\n", + "ax.plot(data_df[plot_item])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "# Sensitivity Analysis" - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Another interesting study to understand if our dataset is indeed helpful in improving certain model parameters is to perform a Sensitivity Analysis. The purpose of this exercise is to scan the Model Parameters of interest (eg, qubit frequency or anharmonicity) across a range of values and notice a prominent dip in the Model Learning Goal Function around the best-fit values" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 41, + "metadata": {}, + "outputs": [], "source": [ "run_name = \"Sensitivity\"\n", "dir_path = \"sensi_logs\"\n", @@ -1480,13 +1484,13 @@ " [-215e6, -205e6],\n", " [4.9985e9, 5.0015e9],\n", "]" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 42, + "metadata": {}, + "outputs": [], "source": [ "sense_opt = Sensitivity(\n", " datafiles=datafiles,\n", @@ -1503,20 +1507,16 @@ ")\n", "\n", "sense_opt.set_exp(exp)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 43, - "source": [ - "sense_opt.run()" - ], + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ "C3:STATUS:Sweeping [['Q1-anhar']]: [-215000000.0, -205000000.0]\n", "C3:STATUS:Saving as: /home/users/anurag/c3/examples/sensi_logs/Sensitivity/2021_07_05_T_20_56_46/sensitivity.log\n", @@ -1525,38 +1525,42 @@ ] } ], - "metadata": {} + "source": [ + "sense_opt.run()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Anharmonicity" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 44, + "metadata": {}, + "outputs": [], "source": [ "LOGDIR = sense_opt.logdir_list[0]" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 45, + "metadata": {}, + "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 46, + "metadata": {}, + "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1564,43 +1568,35 @@ " temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n", " param = temp_dict[\"params\"][0]\n", " data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 47, + "metadata": {}, + "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 48, - "source": [ - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Q1-Anharmonicity [Hz]\")\n", - "ax.set_ylabel(\"Goal Function\")\n", - "ax.axvline(x=best_point_dict[\"Q1-anhar\"], color=\"black\", linestyle=\"-.\")\n", - "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" - ], + "metadata": { + "scrolled": true + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 48, "metadata": {}, - "execution_count": 48 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { "image/png": 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", "text/plain": [ @@ -1609,43 +1605,51 @@ }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": { - "scrolled": true - } + "source": [ + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Q1-Anharmonicity [Hz]\")\n", + "ax.set_ylabel(\"Goal Function\")\n", + "ax.axvline(x=best_point_dict[\"Q1-anhar\"], color=\"black\", linestyle=\"-.\")\n", + "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## Frequency" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 49, + "metadata": {}, + "outputs": [], "source": [ "LOGDIR = sense_opt.logdir_list[1]" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 50, + "metadata": {}, + "outputs": [], "source": [ "logfile = os.path.join(LOGDIR, \"sensitivity.log\")\n", "with open(logfile, \"r\") as f:\n", " log = f.readlines()" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 51, + "metadata": {}, + "outputs": [], "source": [ "data_list_dict = list()\n", "for line in log[9:]:\n", @@ -1653,43 +1657,33 @@ " temp_dict = ast.literal_eval(line.strip(\"\\n\"))\n", " param = temp_dict[\"params\"][0]\n", " data_list_dict.append({\"param\": param, \"goal\": temp_dict[\"goal\"]})" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 52, + "metadata": {}, + "outputs": [], "source": [ "data_df = pd.DataFrame(data_list_dict)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 53, - "source": [ - "fig = plt.figure(figsize=(12, 8))\n", - "ax = fig.add_subplot(111)\n", - "ax.set_xlabel(\"Q1-Frequency [Hz]\")\n", - "ax.set_ylabel(\"Goal Function\")\n", - "ax.axvline(x=best_point_dict[\"Q1-freq\"], color=\"black\", linestyle=\"-.\")\n", - "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" - ], + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "" ] }, + "execution_count": 53, "metadata": {}, - "execution_count": 53 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { "image/png": 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"text/plain": [ @@ -1698,10 +1692,18 @@ }, "metadata": { "needs_background": "light" - } + }, + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "fig = plt.figure(figsize=(12, 8))\n", + "ax = fig.add_subplot(111)\n", + "ax.set_xlabel(\"Q1-Frequency [Hz]\")\n", + "ax.set_ylabel(\"Goal Function\")\n", + "ax.axvline(x=best_point_dict[\"Q1-freq\"], color=\"black\", linestyle=\"-.\")\n", + "ax.scatter(data_df[\"param\"], data_df[\"goal\"])" + ] } ], "metadata": { @@ -1728,4 +1730,4 @@ }, "nbformat": 4, "nbformat_minor": 5 -} \ No newline at end of file +} From c4ac6259173af50189f16cc5979a3ff20b846471 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Wed, 8 Dec 2021 14:58:18 +0100 Subject: [PATCH 51/80] remove deprecated parser and add log_reader --- docs/c3.utils.rst | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/c3.utils.rst b/docs/c3.utils.rst index e0772b35..0e2d0135 100644 --- a/docs/c3.utils.rst +++ b/docs/c3.utils.rst @@ -1,14 +1,6 @@ Utilities package ================== -Parsers module --------------- - -.. automodule:: c3.utils.parsers - :members: - :undoc-members: - :show-inheritance: - Qutip utilities module ----------------------- @@ -25,6 +17,14 @@ Tensorflow utilities module :undoc-members: :show-inheritance: +Log Reader utilities module +---------------------------- + +.. automodule:: c3.utils.log_reader + :members: + :undoc-members: + :show-inheritance: + Miscellaneous utilities module ------------------------------ From 32bdab605dbba2e44190f31a4f71067658b533a0 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Wed, 8 Dec 2021 15:00:00 +0100 Subject: [PATCH 52/80] entangling gates example in docs, closes #157 --- docs/index.rst | 1 + docs/two_qubit_entangling_gate.rst | 698 ++++++++++++++++++ .../two_qubit_entangling_gate_28_0.png | Bin 0 -> 21728 bytes .../two_qubit_entangling_gate_28_1.png | Bin 0 -> 15895 bytes .../two_qubit_entangling_gate_38_0.png | Bin 0 -> 28663 bytes .../two_qubit_entangling_gate_38_1.png | Bin 0 -> 21492 bytes .../two_qubit_entangling_gate_40_1.png | Bin 0 -> 27646 bytes .../two_qubit_entangling_gate_40_2.png | Bin 0 -> 19762 bytes 8 files changed, 699 insertions(+) create mode 100644 docs/two_qubit_entangling_gate.rst create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_28_0.png create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_28_1.png create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_38_0.png create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_38_1.png create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_40_1.png create mode 100644 docs/two_qubit_entangling_gate_files/two_qubit_entangling_gate_40_2.png diff --git a/docs/index.rst b/docs/index.rst index e7e50d42..b31c173a 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -31,6 +31,7 @@ When combined in sequence, these three procedures represent a recipe for system Parametermap two_qubits optimal_control + two_qubit_entangling_gate Simulated_calibration Simulated_Model_Learning log_reader diff --git a/docs/two_qubit_entangling_gate.rst b/docs/two_qubit_entangling_gate.rst new file mode 100644 index 00000000..be539a27 --- /dev/null +++ b/docs/two_qubit_entangling_gate.rst @@ -0,0 +1,698 @@ +Entangling gate on two coupled qubits +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ + +Imports +^^^^^^^ + +.. code:: ipython3 + + !pip install -q -U pip + !pip install -q matplotlib + +.. code:: ipython3 + + # System imports + import copy + import numpy as np + import time + import itertools + import matplotlib.pyplot as plt + import tensorflow as tf + import tensorflow_probability as tfp + from typing import List + + # Main C3 objects + from c3.c3objs import Quantity as Qty + from c3.parametermap import ParameterMap as PMap + from c3.experiment import Experiment as Exp + from c3.model import Model as Mdl + from c3.generator.generator import Generator as Gnr + + # Building blocks + import c3.generator.devices as devices + import c3.signal.gates as gates + import c3.libraries.chip as chip + import c3.signal.pulse as pulse + import c3.libraries.tasks as tasks + + # Libs and helpers + import c3.libraries.algorithms as algorithms + import c3.libraries.hamiltonians as hamiltonians + import c3.libraries.fidelities as fidelities + import c3.libraries.envelopes as envelopes + import c3.utils.qt_utils as qt_utils + import c3.utils.tf_utils as tf_utils + + +Model components +^^^^^^^^^^^^^^^^ + +The model consists of two qubits with 3 levels each and slightly +different parameters: + +.. code:: ipython3 + + qubit_lvls = 3 + freq_q1 = 5e9 + anhar_q1 = -210e6 + t1_q1 = 27e-6 + t2star_q1 = 39e-6 + qubit_temp = 50e-3 + + q1 = chip.Qubit( + name="Q1", + desc="Qubit 1", + freq=Qty(value=freq_q1, min_val=4.995e9, max_val=5.005e9, unit='Hz 2pi'), + anhar=Qty(value=anhar_q1, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'), + hilbert_dim=qubit_lvls, + t1=Qty(value=t1_q1, min_val=1e-6, max_val=90e-6, unit='s'), + t2star=Qty(value=t2star_q1, min_val=10e-6, max_val=90e-3, unit='s'), + temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K') + ) + + freq_q2 = 5.6e9 + anhar_q2 = -240e6 + t1_q2 = 23e-6 + t2star_q2 = 31e-6 + q2 = chip.Qubit( + name="Q2", + desc="Qubit 2", + freq=Qty(value=freq_q2, min_val=5.595e9, max_val=5.605e9, unit='Hz 2pi'), + anhar=Qty(value=anhar_q2, min_val=-380e6, max_val=-120e6, unit='Hz 2pi'), + hilbert_dim=qubit_lvls, + t1=Qty(value=t1_q2, min_val=1e-6, max_val=90e-6,unit='s'), + t2star=Qty(value=t2star_q2, min_val=10e-6, max_val=90e-6, unit='s'), + temp=Qty(value=qubit_temp, min_val=0.0, max_val=0.12, unit='K') + ) + + +There is a static coupling in x-direction between them: +:math:`(b_1+b_1^\dagger)(b_2+b_2^\dagger)` + +.. code:: ipython3 + + coupling_strength = 50e6 + q1q2 = chip.Coupling( + name="Q1-Q2", + desc="coupling", + comment="Coupling qubit 1 to qubit 2", + connected=["Q1", "Q2"], + strength=Qty( + value=coupling_strength, + min_val=-1 * 1e3 , + max_val=200e6 , + unit='Hz 2pi' + ), + hamiltonian_func=hamiltonians.int_XX + ) + +and each qubit has a drive line + +.. code:: ipython3 + + drive1 = chip.Drive( + name="d1", + desc="Drive 1", + comment="Drive line 1 on qubit 1", + connected=["Q1"], + hamiltonian_func=hamiltonians.x_drive + ) + drive2 = chip.Drive( + name="d2", + desc="Drive 2", + comment="Drive line 2 on qubit 2", + connected=["Q2"], + hamiltonian_func=hamiltonians.x_drive + ) + +All parts are collected in the model. The initial state will be thermal +at a non-vanishing temperature. + +.. code:: ipython3 + + init_temp = 50e-3 + init_ground = tasks.InitialiseGround( + init_temp=Qty(value=init_temp, min_val=-0.001, max_val=0.22, unit='K') + ) + + model = Mdl( + [q1, q2], # Individual, self-contained components + [drive1, drive2, q1q2], # Interactions between components + [init_ground] # SPAM processing + ) + model.set_lindbladian(False) + model.set_dressed(True) + +Control signals +^^^^^^^^^^^^^^^ + +The devices for the control line are set up + +.. code:: ipython3 + + sim_res = 100e9 # Resolution for numerical simulation + awg_res = 2e9 # Realistic, limited resolution of an AWG + v2hz = 1e9 + + lo = devices.LO(name='lo', resolution=sim_res) + awg = devices.AWG(name='awg', resolution=awg_res) + mixer = devices.Mixer(name='mixer') + resp = devices.Response( + name='resp', + rise_time=Qty(value=0.3e-9, min_val=0.05e-9, max_val=0.6e-9, unit='s'), + resolution=sim_res + ) + dig_to_an = devices.DigitalToAnalog(name="dac", resolution=sim_res) + v_to_hz = devices.VoltsToHertz( + name='v_to_hz', + V_to_Hz=Qty(value=v2hz, min_val=0.9e9, max_val=1.1e9, unit='Hz/V') + ) + +The generator combines the parts of the signal generation and assignes a +signal chain to each control line. + +.. code:: ipython3 + + generator = Gnr( + devices={ + "LO": lo, + "AWG": awg, + "DigitalToAnalog": dig_to_an, + "Response": resp, + "Mixer": mixer, + "VoltsToHertz": v_to_hz + }, + chains={ + "d1": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"], + "d2": ["LO", "AWG", "DigitalToAnalog", "Response", "Mixer", "VoltsToHertz"] + } + ) + +Gates-set and Parameter map +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Following a general cross resonance scheme, both qubits will be +resonantly driven at the frequency of qubit 2 with a Gaussian envelope. +We drive qubit 1 (the control) at the frequency of qubit 2 (the target) +with a higher amplitude to compensate for the reduced Rabi frequency. + +.. code:: ipython3 + + t_final = 45e-9 + sideband = 50e6 + gauss_params_single_1 = { + 'amp': Qty(value=0.8, min_val=0.2, max_val=3, unit="V"), + 't_final': Qty(value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit="s"), + 'sigma': Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit="s"), + 'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'), + 'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'), + 'delta': Qty(value=-1, min_val=-5, max_val=3, unit="") + } + + gauss_params_single_2 = { + 'amp': Qty(value=0.03, min_val=0.02, max_val=0.6, unit="V"), + 't_final': Qty(value=t_final, min_val=0.5 * t_final, max_val=1.5 * t_final, unit="s"), + 'sigma': Qty(value=t_final / 4, min_val=t_final / 8, max_val=t_final / 2, unit="s"), + 'xy_angle': Qty(value=0.0, min_val=-0.5 * np.pi, max_val=2.5 * np.pi, unit='rad'), + 'freq_offset': Qty(value=-sideband - 3e6, min_val=-56 * 1e6, max_val=-52 * 1e6, unit='Hz 2pi'), + 'delta': Qty(value=-1, min_val=-5, max_val=3, unit="") + } + + gauss_env_single_1 = pulse.Envelope( + name="gauss1", + desc="Gaussian envelope on drive 1", + params=gauss_params_single_1, + shape=envelopes.gaussian_nonorm + ) + gauss_env_single_2 = pulse.Envelope( + name="gauss2", + desc="Gaussian envelope on drive 2", + params=gauss_params_single_2, + shape=envelopes.gaussian_nonorm + ) + +The carrier signal of each drive is set to the resonance frequency of +the target qubit. + +.. code:: ipython3 + + lo_freq_q1 = freq_q1 + sideband + lo_freq_q2 = freq_q2 + sideband + + carr_1 = pulse.Carrier( + name="carrier", + desc="Carrier on drive 1", + params={ + 'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'), + 'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad') + } + ) + + carr_2 = pulse.Carrier( + name="carrier", + desc="Carrier on drive 2", + params={ + 'freq': Qty(value=lo_freq_q2, min_val=0.9 * lo_freq_q2, max_val=1.1 * lo_freq_q2, unit='Hz 2pi'), + 'framechange': Qty(value=0.0, min_val=-np.pi, max_val=3 * np.pi, unit='rad') + } + ) + +Instructions +^^^^^^^^^^^^ + +The instruction to be optimised is a CNOT gates controlled by qubit 1. + +.. code:: ipython3 + + # CNOT comtrolled by qubit 1 + cnot12 = gates.Instruction( + name="cnot12", targets=[0, 1], t_start=0.0, t_end=t_final, channels=["d1", "d2"], + ideal=np.array([ + [1,0,0,0], + [0,1,0,0], + [0,0,0,1], + [0,0,1,0] + ]) + ) + cnot12.add_component(gauss_env_single_1, "d1") + cnot12.add_component(carr_1, "d1") + cnot12.add_component(gauss_env_single_2, "d2") + cnot12.add_component(carr_2, "d2") + cnot12.comps["d1"]["carrier"].params["framechange"].set_value( + (-sideband * t_final) * 2 * np.pi % (2 * np.pi) + ) + +The experiment +^^^^^^^^^^^^^^ + +All components are collected in the parameter map and the experiment is +set up. + +.. code:: ipython3 + + parameter_map = PMap(instructions=[cnot12], model=model, generator=generator) + exp = Exp(pmap=parameter_map) + +Calculate and print the propagator before the optimisation. + +.. code:: ipython3 + + unitaries = exp.compute_propagators() + print(unitaries[cnot12.get_key()]) + + +.. parsed-literal:: + + tf.Tensor( + [[ 5.38699071e-01-7.17750563e-02j -8.34752005e-01+8.73275022e-02j + -6.95346256e-03-2.15875540e-03j -4.35619589e-03+3.35449682e-03j + -1.06942994e-02+4.11831376e-03j -6.46672021e-05-3.73989900e-05j + -1.67838080e-04-2.08026492e-04j -6.43312053e-05-7.70584828e-07j + -3.76227149e-07-6.49845314e-07j] + [-8.22954017e-01+1.64865789e-01j -5.35373070e-01+9.17248769e-02j + -7.01716357e-03+7.68563193e-03j -1.04194796e-02+4.75452421e-03j + -1.61239175e-02-5.34774092e-03j -2.42060738e-04-1.19946128e-05j + 3.81855912e-05+8.66289943e-06j -1.30621879e-04-2.10380577e-04j + -8.82654253e-07-1.33276919e-06j] + [-7.61570279e-03+7.68089055e-04j -4.61417534e-03+9.02462832e-03j + 3.59132066e-01-9.32828470e-01j -9.10153028e-05-6.83262609e-05j + -2.24711912e-04+8.79671466e-05j 2.62921224e-02-1.48696337e-03j + -4.75883791e-04-4.20508543e-05j 3.46114778e-05+1.64470496e-04j + 2.10121296e-04+1.48066297e-04j] + [ 4.65531318e-03-6.63491197e-05j 8.62792565e-03+8.22022317e-03j + -5.58701973e-05+1.08666061e-04j 6.94902895e-02-7.11528641e-01j + -6.81737268e-01-1.53183314e-01j -2.09824678e-03-1.43761730e-03j + 1.48197730e-02-1.51149441e-02j -6.85074400e-03+1.43594091e-03j + 4.07440635e-05-6.43168354e-05j] + [ 9.49155432e-03+6.86731461e-03j 4.92068252e-03+1.60041286e-02j + 1.71300460e-04+1.83910737e-04j -6.94165643e-01-7.98008223e-02j + 1.68675369e-01-6.94722446e-01j 2.75768137e-03-5.72343874e-03j + -6.67593164e-03+1.87532770e-03j 1.07707017e-02+7.28665794e-03j + 1.40030301e-04-6.25646793e-05j] + [ 3.43460967e-05+8.01438338e-05j 1.86345824e-04+1.52916372e-04j + -1.74936595e-02-1.96833938e-02j -2.61695107e-03-5.33671505e-04j + 1.02116861e-03-6.21800378e-03j -4.07849502e-01+9.12571012e-01j + 7.51460471e-05-1.15167196e-04j 2.32056836e-04-2.97650209e-04j + 2.03278960e-04+1.15047574e-02j] + [ 2.54853797e-04-1.25904275e-04j 6.64845849e-05-1.08876861e-05j + 2.38628329e-04-2.95318799e-04j -2.10696691e-02+5.90348860e-05j + 4.21445291e-03+6.01993253e-03j -1.32690530e-04-2.44975772e-05j + 5.90859776e-01+4.84056180e-01j -6.08336007e-01-2.14442516e-01j + 3.13146026e-03+2.83895304e-03j] + [ 2.96366741e-05-8.10052801e-05j 2.39607442e-04-8.47647458e-05j + -2.60360838e-04+2.04175607e-04j 4.95127881e-03+5.19423708e-03j + -5.00047077e-03-1.18242204e-02j -3.71631612e-04-5.78977628e-05j + -6.29480118e-01-1.40758384e-01j -7.57820104e-01+9.68476237e-02j + 1.32060361e-03+7.25998662e-03j] + [ 8.28054635e-07-3.59336781e-07j 1.64602058e-06-1.47364829e-06j + -2.13361477e-04+2.05358711e-04j -5.70978380e-05+4.73283539e-05j + -1.48466829e-04-3.89352221e-06j 1.00811226e-02-5.54615336e-03j + 4.21887172e-03+1.38103179e-03j 3.74182763e-03+6.21303072e-03j + -5.89257172e-01+8.07818774e-01j]], shape=(9, 9), dtype=complex128) + + +Dynamics +^^^^^^^^ + +The system is initialised in the state :math:`|0,1\rangle` so that a +transition to :math:`|1,1\rangle` should be visible. + +.. code:: ipython3 + + psi_init = [[0] * 9] + psi_init[0][0] = 1 + init_state = tf.transpose(tf.constant(psi_init, tf.complex128)) + print(init_state) + + +.. parsed-literal:: + + tf.Tensor( + [[1.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j]], shape=(9, 1), dtype=complex128) + + +.. code:: ipython3 + + def plot_dynamics(exp, psi_init, seq): + """ + Plotting code for time-resolved populations. + + Parameters + ---------- + psi_init: tf.Tensor + Initial state or density matrix. + seq: list + List of operations to apply to the initial state. + """ + model = exp.pmap.model + dUs = exp.partial_propagators + psi_t = psi_init.numpy() + pop_t = exp.populations(psi_t, model.lindbladian) + for gate in seq: + for du in dUs[gate]: + psi_t = np.matmul(du.numpy(), psi_t) + pops = exp.populations(psi_t, model.lindbladian) + pop_t = np.append(pop_t, pops, axis=1) + + fig, axs = plt.subplots(1, 1) + ts = exp.ts + dt = ts[1] - ts[0] + ts = np.linspace(0.0, dt*pop_t.shape[1], pop_t.shape[1]) + axs.plot(ts / 1e-9, pop_t.T) + axs.grid(linestyle="--") + axs.tick_params( + direction="in", left=True, right=True, top=True, bottom=True + ) + axs.set_xlabel('Time [ns]') + axs.set_ylabel('Population') + plt.legend(model.state_labels) + pass + + def getQubitsPopulation(population: np.array, dims: List[int]) -> np.array: + """ + Splits the population of all levels of a system into the populations of levels per subsystem. + Parameters + ---------- + population: np.array + The time dependent population of each energy level. First dimension: level index, second dimension: time. + dims: List[int] + The number of levels for each subsystem. + Returns + ------- + np.array + The time-dependent population of energy levels for each subsystem. First dimension: subsystem index, second + dimension: level index, third dimension: time. + """ + numQubits = len(dims) + + # create a list of all levels + qubit_levels = [] + for dim in dims: + qubit_levels.append(list(range(dim))) + combined_levels = list(itertools.product(*qubit_levels)) + + # calculate populations + qubitsPopulations = np.zeros((numQubits, dims[0], population.shape[1])) + for idx, levels in enumerate(combined_levels): + for i in range(numQubits): + qubitsPopulations[i, levels[i]] += population[idx] + return qubitsPopulations + + def plotSplittedPopulation( + exp: Exp, + psi_init: tf.Tensor, + sequence: List[str] + ) -> None: + """ + Plots time dependent populations for multiple qubits in separate plots. + Parameters + ---------- + exp: Experiment + The experiment containing the model and propagators + psi_init: np.array + Initial state vector + sequence: List[str] + List of gate names that will be applied to the state + ------- + """ + # calculate the time dependent level population + model = exp.pmap.model + dUs = exp.partial_propagators + psi_t = psi_init.numpy() + pop_t = exp.populations(psi_t, model.lindbladian) + for gate in sequence: + for du in dUs[gate]: + psi_t = np.matmul(du, psi_t) + pops = exp.populations(psi_t, model.lindbladian) + pop_t = np.append(pop_t, pops, axis=1) + dims = [s.hilbert_dim for s in model.subsystems.values()] + splitted = getQubitsPopulation(pop_t, dims) + + # timestamps + dt = exp.ts[1] - exp.ts[0] + ts = np.linspace(0.0, dt * pop_t.shape[1], pop_t.shape[1]) + + # create both subplots + titles = list(exp.pmap.model.subsystems.keys()) + fig, axs = plt.subplots(1, len(splitted), sharey="all") + for idx, ax in enumerate(axs): + ax.plot(ts / 1e-9, splitted[idx].T) + ax.tick_params(direction="in", left=True, right=True, top=False, bottom=True) + ax.set_xlabel("Time [ns]") + ax.set_ylabel("Population") + ax.set_title(titles[idx]) + ax.legend([str(x) for x in np.arange(dims[idx])]) + ax.grid() + + plt.tight_layout() + plt.show() + + sequence = [cnot12.get_key()] + plot_dynamics(exp, init_state, sequence) + plotSplittedPopulation(exp, init_state, sequence) + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_28_0.png + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_28_1.png + + +Open-loop optimal control +^^^^^^^^^^^^^^^^^^^^^^^^^ + +Now, open-loop optimisation with DRAG enabled is set up. + +.. code:: ipython3 + + generator.devices['AWG'].enable_drag_2() + + opt_gates = [cnot12.get_key()] + exp.set_opt_gates(opt_gates) + + gateset_opt_map=[ + [(cnot12.get_key(), "d1", "gauss1", "amp")], + [(cnot12.get_key(), "d1", "gauss1", "freq_offset")], + [(cnot12.get_key(), "d1", "gauss1", "xy_angle")], + [(cnot12.get_key(), "d1", "gauss1", "delta")], + [(cnot12.get_key(), "d1", "carrier", "framechange")], + [(cnot12.get_key(), "d2", "gauss2", "amp")], + [(cnot12.get_key(), "d2", "gauss2", "freq_offset")], + [(cnot12.get_key(), "d2", "gauss2", "xy_angle")], + [(cnot12.get_key(), "d2", "gauss2", "delta")], + [(cnot12.get_key(), "d2", "carrier", "framechange")] + ] + parameter_map.set_opt_map(gateset_opt_map) + + parameter_map.print_parameters() + + +.. parsed-literal:: + + cnot12[0, 1]-d1-gauss1-amp : 800.000 mV + cnot12[0, 1]-d1-gauss1-freq_offset : -53.000 MHz 2pi + cnot12[0, 1]-d1-gauss1-xy_angle : -444.089 arad + cnot12[0, 1]-d1-gauss1-delta : -1.000 + cnot12[0, 1]-d1-carrier-framechange : 4.712 rad + cnot12[0, 1]-d2-gauss2-amp : 30.000 mV + cnot12[0, 1]-d2-gauss2-freq_offset : -53.000 MHz 2pi + cnot12[0, 1]-d2-gauss2-xy_angle : -444.089 arad + cnot12[0, 1]-d2-gauss2-delta : -1.000 + cnot12[0, 1]-d2-carrier-framechange : 0.000 rad + + + +As a fidelity function we choose unitary fidelity as well as LBFG-S (a +wrapper of the scipy implementation) from our library. + +.. code:: ipython3 + + import os + import tempfile + from c3.optimizers.optimalcontrol import OptimalControl + + log_dir = os.path.join(tempfile.TemporaryDirectory().name, "c3logs") + opt = OptimalControl( + dir_path=log_dir, + fid_func=fidelities.unitary_infid_set, + fid_subspace=["Q1", "Q2"], + pmap=parameter_map, + algorithm=algorithms.lbfgs, + options={ + "maxfun": 25 + }, + run_name="cnot12" + ) + +Start the optimisation + +.. code:: ipython3 + + exp.set_opt_gates(opt_gates) + opt.set_exp(exp) + opt.optimize_controls() + + +.. parsed-literal:: + + C3:STATUS:Saving as: /tmp/tmpjx66lyg2/c3logs/cnot12/2021_12_08_T_12_27_05/open_loop.log + 1 0.8790556354859858 + 2 0.9673489008768812 + 3 0.758622722337525 + 4 0.7679637459613755 + 5 0.6962301452070802 + 6 0.541321232138175 + 7 0.5682335581707882 + 8 0.382921410272719 + 9 0.43114251105289114 + 10 0.30099424375388173 + 11 0.32449492775751976 + 12 0.26537726105532744 + 13 0.2653362073570743 + 14 0.25121669688810866 + 15 0.23925168937407626 + 16 0.18551042816386099 + 17 0.1305543307431979 + 18 0.07413739981051659 + 19 0.031551815290153495 + 20 0.017447484467834062 + 21 0.007924221221055072 + 22 0.006483318391815374 + 23 0.005732979353259449 + 24 0.005594385264244273 + 25 0.0055582927728303755 + 26 0.005521343169743842 + + +The final parameters and the fidelity are + +.. code:: ipython3 + + parameter_map.print_parameters() + print(opt.current_best_goal) + + +.. parsed-literal:: + + cnot12[0, 1]-d1-gauss1-amp : 2.359 V + cnot12[0, 1]-d1-gauss1-freq_offset : -53.252 MHz 2pi + cnot12[0, 1]-d1-gauss1-xy_angle : 587.818 mrad + cnot12[0, 1]-d1-gauss1-delta : -743.473 m + cnot12[0, 1]-d1-carrier-framechange : -815.216 mrad + cnot12[0, 1]-d2-gauss2-amp : 56.719 mV + cnot12[0, 1]-d2-gauss2-freq_offset : -53.176 MHz 2pi + cnot12[0, 1]-d2-gauss2-xy_angle : -135.515 mrad + cnot12[0, 1]-d2-gauss2-delta : -519.864 m + cnot12[0, 1]-d2-carrier-framechange : 598.919 mrad + + 0.005521343169743842 + + +Results of the optimisation +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Plotting the dynamics with the same initial state: + +.. code:: ipython3 + + plot_dynamics(exp, init_state, sequence) + plotSplittedPopulation(exp, init_state, sequence) + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_38_0.png + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_38_1.png + + +Now we plot the dynamics for the control in the excited state. + +.. code:: ipython3 + + psi_init = [[0] * 9] + psi_init[0][4] = 1 + init_state = tf.transpose(tf.constant(psi_init, tf.complex128)) + print(init_state) + + plot_dynamics(exp, init_state, sequence) + plotSplittedPopulation(exp, init_state, sequence) + + +.. parsed-literal:: + + tf.Tensor( + [[0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [1.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j] + [0.+0.j]], shape=(9, 1), dtype=complex128) + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_40_1.png + + + +.. image:: two_qubit_entangling_gate_files/two_qubit_entangling_gate_40_2.png + + +As intended, the dynamics of the target is dependent on the control 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zR{z)4F^L_#8+MI3eI_doF>U6aRNYDkq`Rq4hwKp9gSBQyt>&_aGYOe=ri4k_2vE~=lGgiIKM*3#!W-Au zVY{d`FQ4%w@NC!~xtkFwO^+QARXfzeZSri9W?TKS*`8)aXQ#DPs!vnvyWFB={8S)& znvLOX>a_sadihHm23#Uv<(w&MZ@G<-A>Xd{Q^th;pXt%HE>S$nAz!3JCxE|9%)yp$ JtODnI Date: Tue, 14 Dec 2021 16:09:39 +0100 Subject: [PATCH 53/80] Correct env in shebang. --- c3/main.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/main.py b/c3/main.py index 51624585..a22d9c28 100755 --- a/c3/main.py +++ b/c3/main.py @@ -1,4 +1,4 @@ -#!/usr/bin/python3 +#!/usr/bin/env python3 """Base script to run the C3 code from a main config file.""" import logging From 7fe69df17bd793b22dbff492e4c5e4d7d7c4c243 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 16:10:54 +0100 Subject: [PATCH 54/80] No error when time field is missing. --- c3/utils/log_reader.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/utils/log_reader.py b/c3/utils/log_reader.py index 1e45bb1e..00aa26a1 100755 --- a/c3/utils/log_reader.py +++ b/c3/utils/log_reader.py @@ -42,7 +42,7 @@ def show_table(log: Dict[str, Any], console: Console) -> None: console.clear() print( - f"Optimization reached {optim_status['goal']:0.3g} at {optim_status['time']}\n" + f"Optimization reached {optim_status.pop('goal', -1):0.3g} at {optim_status.pop('time', 0)}\n" ) console.print(table) From 9d333824e902ca823d2b3ad0ff016bfe562673ee Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 16:11:55 +0100 Subject: [PATCH 55/80] Fixed cosine flattop. --- c3/libraries/envelopes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/c3/libraries/envelopes.py b/c3/libraries/envelopes.py index 95f28c84..e8f0cd0c 100755 --- a/c3/libraries/envelopes.py +++ b/c3/libraries/envelopes.py @@ -453,12 +453,12 @@ def cosine_flattop(t, params): """ t_rise = tf.cast(params["t_rise"].get_value(), tf.float64) dt = t[1] - t[0] - n_rise = tf.cast(t_rise / dt, tf.int32) + n_rise = tf.reshape(tf.cast(t_rise / dt, tf.int32), ()) n_flat = len(t) - 2 * n_rise cos_flt = tf.concat( [ 0.5 * (1 - tf.cos(np.pi * t[:n_rise] / t_rise)), - tf.ones(n_flat, dtype=tf.float64), + tf.ones((n_flat, 1), dtype=tf.float64), 0.5 * (1 + tf.cos(np.pi * t[:n_rise] / t_rise)), ], axis=0, From b76c1ebeffdafb07264a131525e9ed3e21775f7b Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 16:12:46 +0100 Subject: [PATCH 56/80] Initial point handling. --- c3/optimizers/optimalcontrol.py | 6 ++++-- c3/optimizers/optimizer.py | 5 ++++- 2 files changed, 8 insertions(+), 3 deletions(-) diff --git a/c3/optimizers/optimalcontrol.py b/c3/optimizers/optimalcontrol.py index c94e74b0..d3161518 100755 --- a/c3/optimizers/optimalcontrol.py +++ b/c3/optimizers/optimalcontrol.py @@ -50,6 +50,7 @@ def __init__( dir_path=None, callback_fids=None, algorithm=None, + initial_point: str = "", store_unitaries=False, options={}, run_name=None, @@ -63,6 +64,7 @@ def __init__( super().__init__( pmap=pmap, algorithm=algorithm, + initial_point=initial_point, store_unitaries=store_unitaries, logger=logger, ) @@ -117,7 +119,7 @@ def log_setup(self) -> None: if run_name is None: run_name = "c1_" + self.fid_func.__name__ + "_" + self.algorithm.__name__ self.logdir = log_setup(self.__dir_path, run_name) - self.logname = "open_loop.log" + self.logname = "open_loop.c3log" if isinstance(self.exp.created_by, str): shutil.copy2(self.exp.created_by, self.logdir) if isinstance(self.created_by, str): @@ -126,7 +128,7 @@ def log_setup(self) -> None: def load_model_parameters(self, adjust_exp: str) -> None: self.pmap.load_values(adjust_exp) self.pmap.model.update_model() - shutil.copy(adjust_exp, os.path.join(self.logdir, "adjust_exp.log")) + shutil.copy(adjust_exp, os.path.join(self.logdir, "adjust_exp.c3log")) def optimize_controls(self, setup_log: bool = True) -> None: """ diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 51d7acaa..6ca3fe2e 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -32,6 +32,7 @@ class Optimizer: def __init__( self, pmap: ParameterMap, + initial_point: str = "", algorithm: Callable = None, store_unitaries: bool = False, logger: List = None, @@ -49,6 +50,8 @@ def __init__( self.__dir_path: str = "" self.logdir: str = "" self.set_algorithm(algorithm) + if not initial_point == "": + self.load_best(initial_point) self.logger = [] if logger is not None: self.logger = logger @@ -313,7 +316,7 @@ class TensorBoardLogger(BaseLogger): def __init__(self): super().__init__() self.opt_map = [] - self.writer = None + self.writer: None self.store_better_iterations_only = True self.best_iteration = np.inf From b1c2cec50dbd37ddbd223db838664b40bf19d4a1 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 16:14:19 +0100 Subject: [PATCH 57/80] logdir initial value type --- c3/experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/experiment.py b/c3/experiment.py index 1ec58a7a..8cbab875 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -63,7 +63,7 @@ def __init__(self, pmap: ParameterMap = None): self.propagators: Dict[str, tf.Tensor] = {} self.partial_propagators: dict = {} self.created_by = None - self.logdir: str = None + self.logdir: str = "" self.propagate_batch_size = None self.use_control_fields = True self.overwrite_propagators = True # Keep only currently computed propagators From 23188068f0ee8aa6dc44866aca8e4bc2d3b69a20 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 16:52:44 +0100 Subject: [PATCH 58/80] Fixed shapes for concat. --- c3/libraries/envelopes.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/c3/libraries/envelopes.py b/c3/libraries/envelopes.py index e8f0cd0c..b18464e4 100755 --- a/c3/libraries/envelopes.py +++ b/c3/libraries/envelopes.py @@ -452,8 +452,8 @@ def cosine_flattop(t, params): """ t_rise = tf.cast(params["t_rise"].get_value(), tf.float64) - dt = t[1] - t[0] - n_rise = tf.reshape(tf.cast(t_rise / dt, tf.int32), ()) + dt = tf.reshape(t[1] - t[0], ()) + n_rise = tf.cast(t_rise / dt, tf.int32) n_flat = len(t) - 2 * n_rise cos_flt = tf.concat( [ From ee48e7a74d4cbe7ca44a18e7bb8f780fe7854a0b Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 17:49:41 +0100 Subject: [PATCH 59/80] Removed asserts. --- c3/c3objs.py | 13 ++++++------- c3/experiment.py | 10 ++++++---- c3/parametermap.py | 12 ++++++++---- 3 files changed, 20 insertions(+), 15 deletions(-) diff --git a/c3/c3objs.py b/c3/c3objs.py index 107986f9..f10f08cf 100755 --- a/c3/c3objs.py +++ b/c3/c3objs.py @@ -273,13 +273,12 @@ def set_value(self, val, extend_bounds=False) -> None: self.set_limits(min_val, max_val) tmp = 2 * (val * self.pref - self.offset) / self.scale - 1 - tf.debugging.assert_less_equal( - tf.math.abs(tmp), - tf.constant(1.0, tf.float64), - f"Value {val.numpy()}{self.unit} out of bounds for quantity with " - f"min_val: {num3str(self.get_limits()[0])}{self.unit} and " - f"max_val: {num3str(self.get_limits()[1])}{self.unit}", - ) + if np.any(tf.math.abs(tmp) > tf.constant(1.0, tf.float64)): + raise Exception( + f"Value {val.numpy()}{self.unit} out of bounds for quantity with " + f"min_val: {num3str(self.get_limits()[0])}{self.unit} and " + f"max_val: {num3str(self.get_limits()[1])}{self.unit}", + ) self.value = tf.cast(tmp, tf.float64) diff --git a/c3/experiment.py b/c3/experiment.py index 8cbab875..85c1645f 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -504,12 +504,14 @@ def propagation(self, signal: dict, gate): ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) signals = None hks = None - assert np.all( + if not np.all( tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0]) - ) - assert np.all( + ): + raise Exception("C3Error:Something with the times happend.") + if not np.all( tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) - ) + ): + raise Exception("C3Error:Something with the times happend.") # TODO: is this compatible with lindbladian if model.max_excitations: diff --git a/c3/parametermap.py b/c3/parametermap.py index 9557f8f3..58c40a52 100644 --- a/c3/parametermap.py +++ b/c3/parametermap.py @@ -209,7 +209,10 @@ def check_limits(self, opt_map): if len(equiv_ids) > 1: limit = self.__pars[equiv_ids[0]].get_limits() for key in equiv_ids[1:]: - assert self.__pars[key].get_limits() == limit + if not self.__pars[key].get_limits() == limit: + raise Exception( + "C3:Error:Limits for {key} are not equivalent to {equiv_ids}." + ) def get_parameter(self, par_id: Tuple[str, ...]) -> Quantity: """ @@ -289,9 +292,10 @@ def set_parameters( model_updated = False val_indx = 0 opt_map = self.get_opt_map(opt_map) - assert len(values) == len( - opt_map - ), "Different number of elements in values and opt_map" + if not len(values) == len(opt_map): + raise Exception( + f"C3:Error: Different number of elements in values and opt_map. {len(values)} vs {len(opt_map)}" + ) for equiv_ids in opt_map: for key in equiv_ids: # We check if a model parameter has changed From 58099120e33c401fc5e2ef7bde09e134c0abc830 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Tue, 14 Dec 2021 17:50:15 +0100 Subject: [PATCH 60/80] Workaround for inconsistent shapes. --- test/test_envelopes.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/test/test_envelopes.py b/test/test_envelopes.py index f3927748..d9f2ecbc 100644 --- a/test/test_envelopes.py +++ b/test/test_envelopes.py @@ -282,10 +282,15 @@ def test_cosine(): } np.testing.assert_allclose( - actual=envelopes["cosine_flattop"](t=ts, params=params), + actual=np.reshape( + envelopes["cosine_flattop"](t=np.reshape(ts, (-1, 1)), params=params), (-1,) + ), desired=test_data["cosine_flattop"], atol=get_atol("cosine_flattop"), ) + # Nico: to be consistent with the signal generation code, the resphapes above are necessary. Somewhere in the + # masking the time vector gets an additional dimension. It all works fine since it's elementwise, but the + # flattop implementation has a concat in it that is strict about shapes. This should be investigated. @pytest.mark.unit From 5c54a098c0815920b4aa961134a0f90f09b07ec4 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 15 Dec 2021 16:10:55 +0100 Subject: [PATCH 61/80] No more asserts. --- c3/generator/devices.py | 21 +++++++++++---------- c3/optimizers/optimizer.py | 5 ++++- c3/signal/gates.py | 9 ++++----- 3 files changed, 19 insertions(+), 16 deletions(-) diff --git a/c3/generator/devices.py b/c3/generator/devices.py index 55d10eac..452970dd 100755 --- a/c3/generator/devices.py +++ b/c3/generator/devices.py @@ -112,12 +112,10 @@ def create_ts( num = self.slice_num + 1 t_start = tf.constant(t_start + offset, dtype=tf.float64) t_end = tf.constant(t_end - offset, dtype=tf.float64) - np.testing.assert_almost_equal( - np.mod(t_end, dt), - 0, - decimal=7, - err_msg="Given length of is not a multiple of the resolution", - ) + if np.mod(t_end, dt) > 1e-7: + raise Exception( + "C3:Error:Given length of is not a multiple of the resolution" + ) # ts = tf.range(t_start, t_end + 1e-16, dt) ts = tf.linspace(t_start, t_end, num) @@ -603,9 +601,11 @@ def process(self, instr, chan, iq_signal): Bandwidth limited IQ signal. """ - np.testing.assert_almost_equal( - actual=iq_signal["ts"][1] - iq_signal["ts"][0], desired=1 / self.resolution - ) + res_diff = (iq_signal["ts"][1] - iq_signal["ts"][0]) / self.resolution - 1 + if res_diff > 1e-8: + raise Exception( + "C3:Error:Actual time resolution differs from desired by {res_diff:1.3g}." + ) n_ts = tf.floor(self.params["rise_time"].get_value() * self.resolution) ts = tf.linspace( tf.constant(0.0, dtype=tf.float64), @@ -1056,7 +1056,8 @@ def process(self, instr: Instruction, chan: str) -> dict: self.signal["inphase"] = cos self.signal["quadrature"] = sin self.signal["ts"] = ts - assert "inphase" in self.signal, f"Probably no carrier proviced for {self.name}" + if "inphase" not in self.signal: + raise Exception(f"C3:Error: Probably no carrier proviced for {self.name}") return self.signal diff --git a/c3/optimizers/optimizer.py b/c3/optimizers/optimizer.py index 6ca3fe2e..4ee4303c 100755 --- a/c3/optimizers/optimizer.py +++ b/c3/optimizers/optimizer.py @@ -321,7 +321,10 @@ def __init__(self): self.best_iteration = np.inf def write_params(self, params, step=0): - assert len(self.opt_map) == len(params) + if not len(self.opt_map) == len(params): + raise Exception( + f"C3:Error: Different number of elements in opt_map and params. {len(self.opt_map)} vs {len(params)}" + ) for i in range(len(self.opt_map)): for key in self.opt_map[i]: if type(params[i]) is float: diff --git a/c3/signal/gates.py b/c3/signal/gates.py index d1dae962..6ceca803 100755 --- a/c3/signal/gates.py +++ b/c3/signal/gates.py @@ -344,11 +344,10 @@ def get_awg_signal(self, chan, ts, options=None): elif "pwc" in options and options["pwc"]: inphase = comp.params["inphase"].get_value() quadrature = comp.params["quadrature"].get_value() - tf.debugging.assert_shapes( - inphase, - quadrature, - message="inphase and quadrature are of different lengths.", - ) + if not inphase.shape == quadrature.shape: + raise Exception( + "C3:Error:inphase and quadrature are of different lengths." + ) env = tf.complex(inphase, quadrature) len_diff = len(ts) - len(env) if len_diff > 0: From 8a687487b22b8930b64b19e65ca42bcea2124cff Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 15 Dec 2021 17:45:44 +0100 Subject: [PATCH 62/80] Reduced complexity. --- c3/c3objs.py | 49 ++++++++++++++++++++++++++++--------------------- 1 file changed, 28 insertions(+), 21 deletions(-) diff --git a/c3/c3objs.py b/c3/c3objs.py index f10f08cf..31f69028 100755 --- a/c3/c3objs.py +++ b/c3/c3objs.py @@ -123,57 +123,57 @@ def asdict(self) -> dict: def __add__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() + other, extend_bounds=True) + out_val._set_value_extend(self.get_value() + other) return out_val def __radd__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() + other, extend_bounds=True) + out_val._set_value_extend(self.get_value() + other) return out_val def __sub__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() - other, extend_bounds=True) + out_val._set_value_extend(self.get_value() - other) return out_val def __rsub__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(other - self.get_value(), extend_bounds=True) + out_val._set_value_extend(other - self.get_value()) return out_val def __mul__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() * other, extend_bounds=True) + out_val._set_value_extend(self.get_value() * other) return out_val def __rmul__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() * other, extend_bounds=True) + out_val._set_value_extend(self.get_value() * other) return out_val def __pow__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() ** other, extend_bounds=True) + out_val._set_value_extend(self.get_value() ** other) return out_val def __rpow__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(other ** self.get_value(), extend_bounds=True) + out_val._set_value_extend(other ** self.get_value()) return out_val def __truediv__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() / other, extend_bounds=True) + out_val._set_value_extend(self.get_value() / other) return out_val def __rtruediv__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(other / self.get_value(), extend_bounds=True) + out_val._set_value_extend(other / self.get_value()) return out_val def __mod__(self, other): out_val = copy.deepcopy(self) - out_val.set_value(self.get_value() % other, extend_bounds=True) + out_val._set_value_extend(self.get_value() % other) return out_val def __lt__(self, other): @@ -253,7 +253,13 @@ def get_value(self, val: tf.float64 = None, dtype: tf.dtypes = None) -> tf.Tenso value = self.scale * (val + 1) / 2 + self.offset return tf.cast(value, dtype) - def set_value(self, val, extend_bounds=False) -> None: + def set_value(self, val, extend_bounds=False): + if extend_bounds: + self._set_value_extend(val) + else: + self._set_value(val) + + def _set_value(self, val) -> None: """Set the value of this quantity as tensorflow. Value needs to be within specified min and max.""" # setting can be numpyish @@ -264,15 +270,6 @@ def set_value(self, val, extend_bounds=False) -> None: tmp = 2 * (val * self.pref - self.offset) / self.scale - 1 - if extend_bounds and tf.math.abs(tmp) > 1: - min_val, max_val = self.get_limits() - # Extra bounds included to not be directly at border due to differentiability - minmax = [val * 0.9, val * 1.1, min_val, max_val] - min_val = tf.math.reduce_min(minmax) - max_val = tf.math.reduce_max(minmax) - self.set_limits(min_val, max_val) - tmp = 2 * (val * self.pref - self.offset) / self.scale - 1 - if np.any(tf.math.abs(tmp) > tf.constant(1.0, tf.float64)): raise Exception( f"Value {val.numpy()}{self.unit} out of bounds for quantity with " @@ -282,6 +279,16 @@ def set_value(self, val, extend_bounds=False) -> None: self.value = tf.cast(tmp, tf.float64) + def _set_value_extend(self, val) -> None: + """Set the value of this quantity as tensorflow. If needed, limits will be extended.""" + min_val, max_val = self.get_limits() + # Extra bounds included to not be directly at border due to differentiability + minmax = [val * 0.9, val * 1.1, min_val, max_val] + min_val = tf.math.reduce_min(minmax) + max_val = tf.math.reduce_max(minmax) + self.set_limits(min_val, max_val) + self._set_value(val) + def get_opt_value(self) -> np.ndarray: """Get an optimizer friendly representation of the value.""" return self.value.numpy().flatten() From 701240a9898b568ed5780cbdb96818b25c103692 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Thu, 16 Dec 2021 16:52:52 +0100 Subject: [PATCH 63/80] remove unused dependencies, closes #159 --- requirements.txt | 3 --- 1 file changed, 3 deletions(-) diff --git a/requirements.txt b/requirements.txt index 244e1294..a8d1708c 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,6 +1,5 @@ adaptive>=0.11.1 cma>=3.0.3 -docstr-coverage>=1.2.0 gast>=0.3.3 hjson>=3.0.2 numpy>=1.19.5 @@ -9,7 +8,6 @@ pytest-cov>=2.11.1 pytest-xdist>=2.2.1 python-dateutil>=2.8.1 qiskit>=0.25.0 -radon>=4.3.2 rich>=9.2.0 scipy>=1.5.2 six>=1.15.0 @@ -18,4 +16,3 @@ sphinx-autoapi>=1.4.0 tensorflow>=2.4.1 tensorflow-estimator>=2.4.0 tensorflow-probability>=0.12.1 -xenon>=0.7.1 From f2e8ee303f2c867774c3492450c6b0b0d27477e6 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Thu, 16 Dec 2021 17:06:29 +0100 Subject: [PATCH 64/80] introduced function for returning Hs of t, move to model later --- c3/libraries/propagation.py | 238 +++++++++++++++++------------------- 1 file changed, 115 insertions(+), 123 deletions(-) diff --git a/c3/libraries/propagation.py b/c3/libraries/propagation.py index 867de8e1..795209fe 100644 --- a/c3/libraries/propagation.py +++ b/c3/libraries/propagation.py @@ -37,96 +37,142 @@ def state_deco(func): @unitary_deco -def gen_dUs_RK4(model: Model, gen: Generator, instr: Instruction, init_state=None): - h0, hctrls = model.get_Hamiltonians() - gen.resolution = 2 * gen.resolution - signal = gen.generate_signals(instr) - signals = [] - hks = [] - for key in signal: - signals.append(signal[key]["values"]) - ts = signal[key]["ts"] - hks.append(hctrls[key]) - signals = tf.cast(signals, tf.complex128) - hks = tf.cast(hks, tf.complex128) +def gen_dUs_RK4(h, dt, dim=None): + dUs = [] + dU = [] + if dim is None: + tot_dim = tf.shape(h) + dim = tot_dim[1] - dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) + for jj in range(0, len(h) - 2, 2): + dU = gen_dU_rk4(h[jj : jj + 3], dt, dim) + dUs.append(dU) + return dUs - U = [] - tot_dim = tf.shape(h0) - dim = tot_dim[1] + +def gen_dU_rk4(h, dt, dim): temp = [] - if hks is not None: - h = h0 - ii = 0 - while ii < len(hks): - h += signals[ii] * hks[ii] - ii += 1 + for ii in range(dim): + psi = tf.one_hot(ii, dim, dtype=tf.complex128) + psi = rk4_step(h, psi, dt) + temp.append(psi) + dU = tf.stack(temp) + return dU - else: - h = h0 - for jj in range(0, len(h) - 2, 2): - dU = [] - for ii in range(dim): - psi = tf.one_hot(ii, dim, dtype=tf.complex128) - - k1 = step_vonNeumann_psi(psi, h[jj], dt) - k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[jj + 1], dt) - k3 = step_vonNeumann_psi(psi + k2 / 2.0, h[jj + 1], dt) - k4 = step_vonNeumann_psi(psi + k3, h[jj + 2], dt) - - psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 - dU.append(psi) - temp = tf.stack(dU) - U.append(temp) - return U +def rk4_step(h, psi, dt): + k1 = step_vonNeumann_psi(psi, h[0], dt) + k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[1], dt) + k3 = step_vonNeumann_psi(psi + k2 / 2.0, h[1], dt) + k4 = step_vonNeumann_psi(psi + k3, h[2], dt) + psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 + return psi -@unitary_deco -def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None): - gen.resolution = 2 * gen.resolution + +def get_Hs_of_t_ts( + model: Model, gen: Generator, instr: Instruction, prop_res=1 +) -> Dict: + """ + Return a Dict containing: + + - a list of + + H(t) = H_0 + sum_k c_k H_k. + + - time slices ts + + - timestep dt + + Parameters + ---------- + prop_res : tf.float + resolution required by the propagation method + h0 : tf.tensor + Drift Hamiltonian. + hks : list of tf.tensor + List of control Hamiltonians. + cflds_t : array of tf.float + Vector of control field values at time t. + ts : float + Length of one time slice. + """ + Hs = [] + ts = [] + gen.resolution = prop_res * gen.resolution signal = gen.generate_signals(instr) if model.controllability: h0, hctrls = model.get_Hamiltonians() - signals = [] hks = [] for key in signal: signals.append(signal[key]["values"]) ts = signal[key]["ts"] hks.append(hctrls[key]) - signals = tf.cast(signals, tf.complex128) + cflds = tf.cast(signals, tf.complex128) hks = tf.cast(hks, tf.complex128) + for ii in range(cflds[0].shape[0]): + cf_t = [] + for fields in cflds: + cf_t.append(tf.cast(fields[ii], tf.complex128)) + Hs.append(sum_h0_hks(h0, hks, cf_t)) + else: + Hs = model.get_Hamiltonian(signal) + ts_list = [sig["ts"][1:] for sig in signal.values()] + ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) + signals = None + hks = None + assert np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])) + assert np.all( + tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) + ) + dt = tf.constant(ts[1 * prop_res].numpy() - ts[0].numpy(), dtype=tf.complex128) + return {"Hs": Hs, "ts": ts[::prop_res], "dt": dt} - dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) - U = [] - tot_dim = tf.shape(h0) - dim = tot_dim[1] +def sum_h0_hks(h0, hks, cf_t): + """ + Compute and Return - if hks is not None: - h = h0 - ii = 0 - while ii < len(hks): - h += signals[ii] * hks[ii] - ii += 1 + H(t) = H_0 + sum_k c_k H_k. + """ + h_of_t = h0 + ii = 0 + while ii < len(hks): + h_of_t += cf_t[ii] * hks[ii] + ii += 1 + return h_of_t - else: - h = model.get_Hamiltonian(signal) - tot_dim = tf.shape(h) - dim = tot_dim[1] +@unitary_deco +def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Dict: + prop_res = 2 + dim = model.tot_dim + Hs = [] + ts = [] + dUs = [] + dict_vals = get_Hs_of_t_ts(model, gen, instr, prop_res) + Hs = dict_vals["Hs"] + ts = dict_vals["ts"] + dt = dict_vals["dt"] + + dUs = gen_dUs_RK4(Hs, dt, dim) + + U = gen_U_RK4(Hs, dt, dim) + + if model.max_excitations: + U = model.blowup_excitations(U) + dUs = tf.vectorized_map(model.blowup_excitations, dUs) + + return {"U": U, "dUs": dUs, "ts": ts} + + +def gen_U_RK4(h, dt, dim): + U = [] for ii in range(dim): psi = tf.one_hot(ii, dim, dtype=tf.complex128) for jj in range(0, len(h) - 2, 2): - - k1 = step_vonNeumann_psi(psi, h[jj], dt) - k2 = step_vonNeumann_psi(psi + k1 / 2.0, h[jj + 1], dt) - k3 = step_vonNeumann_psi(psi + k2 / 2.0, h[jj + 1], dt) - k4 = step_vonNeumann_psi(psi + k3, h[jj + 2], dt) - - psi += (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 + psi = rk4_step(h[jj : jj + 3], psi, dt) U.append(psi) U = tf.stack(U) return tf.transpose(U) @@ -135,7 +181,7 @@ def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None): @unitary_deco def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: """ - Solve the equation of motion (Lindblad or Schrödinger) for a given control + Solve the equation of motion (Lindblad or Schrรถdinger) for a given control signal and Hamiltonians. Parameters @@ -151,6 +197,8 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: Matrix representation of the gate. """ signal = gen.generate_signals(instr) + # Why do I get 0.0 if I print gen.resolution here?! FR + ts = [] if model.controllability: h0, hctrls = model.get_Hamiltonians() signals = [] @@ -171,7 +219,6 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: assert np.all( tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) ) - dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) batch_size = tf.constant(len(h0), tf.int32) @@ -187,61 +234,6 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: return {"U": U, "dUs": dUs, "ts": ts} -# Reference for cutting excitations: -# if model.max_excitations: -# cutter = model.ex_cutter -# hamiltonian = cutter @ hamiltonian @ cutter.T -# if hks is not None: -# cutter_tf = tf.cast(cutter, tf.complex128) -# hks = tf.matmul(cutter_tf, tf.matmul(hks, cutter_tf, transpose_b=True)) - -# dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) - -# if model.lindbladian: -# col_ops = model.get_Lindbladians() -# if model.max_excitations: -# cutter = model.ex_cutter -# col_ops = [cutter @ col_op @ cutter.T for col_op in col_ops] -# dUs = tf_propagation_lind(hamiltonian, hks, col_ops, signals, dt) -# else: -# batch_size = ( -# self.propagate_batch_size -# if self.propagate_batch_size -# else len(hamiltonian) -# ) -# batch_size = ( -# len(hamiltonian) if batch_size > len(hamiltonian) else batch_size -# ) -# batch_size = tf.constant(batch_size, tf.int32) -# dUs = tf_batch_propagate( -# hamiltonian, hks, signals, dt, batch_size=batch_size -# ) - -# U = tf_matmul_left(tf.cast(dUs, tf.complex128)) -# if model.max_excitations: -# U = cutter.T @ U @ cutter -# ex_cutter = tf.cast(tf.expand_dims(model.ex_cutter, 0), tf.complex128) -# if self.stop_partial_propagator_gradient: -# self.partial_propagators[gate] = tf.stop_gradient( -# tf.linalg.matmul( -# tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), -# ex_cutter, -# ) -# ) -# else: -# self.partial_propagators[gate] = tf.stop_gradient( -# tf.linalg.matmul( -# tf.linalg.matmul(tf.linalg.matrix_transpose(ex_cutter), dUs), -# ex_cutter, -# ) -# ) -# else: -# self.partial_propagators[gate] = dUs - -# self.ts = ts -# return U - - # @state_deco # def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Dict[str, List[tf.Tensor]]: # if init_state is None: @@ -276,7 +268,7 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: @tf.function def tf_dU_of_t(h0, hks, cflds_t, dt): """ - Compute H(t) = H_0 + sum_k c_k H_k and matrix exponential exp(i H(t) dt). + Compute H(t) = H_0 + sum_k c_k H_k and matrix exponential exp(-i H(t) dt). Parameters ---------- From 1ad27a0124891d6be71551c747b469931b3f66d8 Mon Sep 17 00:00:00 2001 From: Jurek Frey Date: Wed, 4 Aug 2021 17:09:03 +0200 Subject: [PATCH 65/80] Added n_eval --- c3/libraries/fidelities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/libraries/fidelities.py b/c3/libraries/fidelities.py index 233798d3..92e6cee3 100755 --- a/c3/libraries/fidelities.py +++ b/c3/libraries/fidelities.py @@ -47,7 +47,7 @@ def fid_reg_deco(func): @fid_reg_deco def state_transfer_infid_set( - propagators: dict, instructions: dict, index, dims, psi_0, proj=True + propagators: dict, instructions: dict, index, dims, n_eval=-1, psi_0=None, proj=True ): """ Mean state transfer infidelity. From ed2f94019b6817e96fbf61dbbe0445368163f708 Mon Sep 17 00:00:00 2001 From: Jurek Frey Date: Fri, 22 Oct 2021 06:42:24 +0200 Subject: [PATCH 66/80] fixed initial state --- c3/libraries/fidelities.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/libraries/fidelities.py b/c3/libraries/fidelities.py index 92e6cee3..f8eef4e8 100755 --- a/c3/libraries/fidelities.py +++ b/c3/libraries/fidelities.py @@ -47,7 +47,7 @@ def fid_reg_deco(func): @fid_reg_deco def state_transfer_infid_set( - propagators: dict, instructions: dict, index, dims, n_eval=-1, psi_0=None, proj=True + propagators: dict, instructions: dict, index, dims, psi_0, n_eval=-1, proj=True ): """ Mean state transfer infidelity. From b4f50c0f8f07f451aad3bb48ef8f8249a9e72ba3 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Mon, 25 Oct 2021 17:52:55 +0200 Subject: [PATCH 67/80] update CHANGELOG --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index e3cad650..389c3612 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -19,6 +19,7 @@ This Changelog tracks all past changes to this project as well as details about - `added` human readable saving of current best point for the optimizer #140 - `fixed` handling of anharmonicity in transmons with two levels #146 - `added` an example notebook with entangling two-qubit gates #154 +- `fixed` Broken State Fidelity #135 ## Version `1.3` - 20 Jul 2021 From ac5694d32bcc933a6a459b83a06681aebacae2bf Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 17 Dec 2021 16:47:54 +0100 Subject: [PATCH 68/80] Added decorators for different types of fids. --- c3/libraries/fidelities.py | 58 ++++++++++++++++++++++++++++++++++++-- 1 file changed, 55 insertions(+), 3 deletions(-) diff --git a/c3/libraries/fidelities.py b/c3/libraries/fidelities.py index f8eef4e8..04382618 100755 --- a/c3/libraries/fidelities.py +++ b/c3/libraries/fidelities.py @@ -21,9 +21,7 @@ tf_state_to_dm, ) -from c3.libraries.propagation import ( - evaluate_sequences, -) +from c3.libraries.propagation import evaluate_sequences from c3.utils.qt_utils import ( basis, @@ -34,6 +32,12 @@ cliffords_string, ) +state_providers = dict() +unitary_providers = dict() +set_providers = dict() +super_providers = dict() + +# Compatibility for legacy, i.e. not sorted yet fidelities = dict() @@ -45,7 +49,40 @@ def fid_reg_deco(func): return func +def state_deco(func): + """ + Decorator for making registry of functions + """ + state_providers[str(func.__name__)] = func + return func + + +def unitary_deco(func): + """ + Decorator for making registry of functions + """ + unitary_providers[str(func.__name__)] = func + return func + + +def set_deco(func): + """ + Decorator for making registry of functions + """ + set_providers[str(func.__name__)] = func + return func + + +def open_system_deco(func): + """ + Decorator for making registry of functions + """ + super_providers[str(func.__name__)] = func + return func + + @fid_reg_deco +@state_deco def state_transfer_infid_set( propagators: dict, instructions: dict, index, dims, psi_0, n_eval=-1, proj=True ): @@ -79,6 +116,7 @@ def state_transfer_infid_set( @fid_reg_deco +@state_deco def state_transfer_infid(ideal: np.array, actual: tf.constant, index, dims, psi_0): """ Single gate state transfer infidelity. The dimensions of psi_0 and ideal need to be @@ -112,6 +150,7 @@ def state_transfer_infid(ideal: np.array, actual: tf.constant, index, dims, psi_ @fid_reg_deco +@unitary_deco def unitary_infid( ideal: np.array, actual: tf.Tensor, index: List[int] = None, dims=None ) -> tf.Tensor: @@ -145,6 +184,8 @@ def unitary_infid( @fid_reg_deco +@unitary_deco +@set_deco def unitary_infid_set(propagators: dict, instructions: dict, index, dims, n_eval=-1): """ Mean unitary overlap between ideal and actually performed gate for the gates in @@ -178,6 +219,7 @@ def unitary_infid_set(propagators: dict, instructions: dict, index, dims, n_eval @fid_reg_deco +@open_system_deco def lindbladian_unitary_infid( ideal: np.array, actual: tf.constant, index=[0], dims=[2] ) -> tf.constant: @@ -208,6 +250,8 @@ def lindbladian_unitary_infid( @fid_reg_deco +@open_system_deco +@set_deco def lindbladian_unitary_infid_set( propagators: dict, instructions: Dict[str, Instruction], index, dims, n_eval ): @@ -242,6 +286,7 @@ def lindbladian_unitary_infid_set( @fid_reg_deco +@open_system_deco def average_infid( ideal: np.array, actual: tf.Tensor, index: List[int] = [0], dims=[2] ) -> tf.constant: @@ -267,6 +312,8 @@ def average_infid( @fid_reg_deco +@open_system_deco +@set_deco def average_infid_set( propagators: dict, instructions: dict, index: List[int], dims, n_eval=-1 ): @@ -298,6 +345,8 @@ def average_infid_set( @fid_reg_deco +@open_system_deco +@set_deco def average_infid_seq(propagators: dict, instructions: dict, index, dims, n_eval=-1): """ Average sequence fidelity over all gates in propagators. @@ -326,6 +375,7 @@ def average_infid_seq(propagators: dict, instructions: dict, index, dims, n_eval @fid_reg_deco +@open_system_deco def lindbladian_average_infid( ideal: np.array, actual: tf.constant, index=[0], dims=[2] ) -> tf.constant: @@ -351,6 +401,8 @@ def lindbladian_average_infid( @fid_reg_deco +@open_system_deco +@set_deco def lindbladian_average_infid_set( propagators: dict, instructions: Dict[str, Instruction], index, dims, n_eval ): From 9a630338637507ee399071cee12d492bb180809f Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Fri, 17 Dec 2021 16:48:24 +0100 Subject: [PATCH 69/80] Added test for set fidelities. --- test/test_fidelities.py | 38 +++++++++++++++++++++++++++++++++++++- 1 file changed, 37 insertions(+), 1 deletion(-) diff --git a/test/test_fidelities.py b/test/test_fidelities.py index bced5931..e2f17d0b 100644 --- a/test/test_fidelities.py +++ b/test/test_fidelities.py @@ -2,7 +2,13 @@ import pytest from numpy.testing import assert_array_almost_equal as almost_equal -from c3.libraries.fidelities import unitary_infid, average_infid +from c3.signal.gates import Instruction +from c3.libraries.fidelities import ( + unitary_infid, + average_infid, + unitary_infid_set, + state_transfer_infid_set, +) from c3.libraries.constants import GATES @@ -128,3 +134,33 @@ def test_average_infid_projection_5() -> None: Id, ) almost_equal(average_infid(ideal=X, actual=actual, index=[0], dims=[3, 2]), 0) + + +@pytest.mark.unit +def test_set_unitary() -> None: + propagators = {"rxp": X, "ryp": Y} + instructions = {"rxp": Instruction("rxp"), "ryp": Instruction("ryp")} + goal = unitary_infid_set( + propagators=propagators, + instructions=instructions, + index=[0], + dims=[2], + n_eval=136, + ) + almost_equal(goal, 0) + + +@pytest.mark.unit +def test_set_states() -> None: + propagators = {"rxp": X, "ryp": Y} + instructions = {"rxp": Instruction("rxp"), "ryp": Instruction("ryp")} + psi_0 = np.array([[1], [0]]) + goal = state_transfer_infid_set( + propagators=propagators, + instructions=instructions, + index=[0], + dims=[2], + psi_0=psi_0, + n_eval=136, + ) + almost_equal(goal, 0) From 7be217ff4d999a7c489f14df11e2831deaba0ae2 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Tue, 21 Dec 2021 16:26:02 +0100 Subject: [PATCH 70/80] Added total dimensions. --- c3/model.py | 1 + 1 file changed, 1 insertion(+) diff --git a/c3/model.py b/c3/model.py index 4c3ea44c..e5bfc9a6 100755 --- a/c3/model.py +++ b/c3/model.py @@ -112,6 +112,7 @@ def __create_labels(self) -> None: comp_state_labels.append([0, 1]) self.names = names self.dims = dims + self.tot_dim = int(np.prod(dims)) self.state_labels = list(itertools.product(*state_labels)) self.comp_state_labels = list(itertools.product(*comp_state_labels)) From d18d97d675fababd2bd377b827aa204c7b9b6cb0 Mon Sep 17 00:00:00 2001 From: frosati1 Date: Tue, 21 Dec 2021 17:07:15 +0100 Subject: [PATCH 71/80] Test for basic rk4. --- c3/libraries/algorithms.py | 18 ++--- c3/libraries/chip.py | 48 ++++++------ c3/libraries/fidelities.py | 22 +++--- c3/libraries/propagation.py | 148 ++++++++++++++++++++---------------- test/test_two_qubits.py | 7 ++ 5 files changed, 135 insertions(+), 108 deletions(-) diff --git a/c3/libraries/algorithms.py b/c3/libraries/algorithms.py index 69eadeb3..a7d3e6db 100755 --- a/c3/libraries/algorithms.py +++ b/c3/libraries/algorithms.py @@ -236,7 +236,7 @@ def fun1d(x): @algo_reg_deco def tf_sgd( - x_init: np.array, + x_init: np.ndarray, fun: Callable = None, fun_grad: Callable = None, grad_lookup: Callable = None, @@ -247,7 +247,7 @@ def tf_sgd( Parameters ---------- - x_init : np.array + x_init : np.ndarray starting value of parameter(s) fun : Callable, optional function to minimize, by default None @@ -294,7 +294,7 @@ def tf_fun(): @algo_reg_deco def tf_adam( - x_init: np.array, + x_init: np.ndarray, fun: Callable = None, fun_grad: Callable = None, grad_lookup: Callable = None, @@ -305,7 +305,7 @@ def tf_adam( Parameters ---------- - x_init : np.array + x_init : np.ndarray starting value of parameter(s) fun : Callable, optional function to minimize, by default None @@ -326,7 +326,7 @@ def tf_adam( def tf_rmsprop( - x_init: np.array, + x_init: np.ndarray, fun: Callable = None, fun_grad: Callable = None, grad_lookup: Callable = None, @@ -337,7 +337,7 @@ def tf_rmsprop( Parameters ---------- - x_init : np.array + x_init : np.ndarray starting value of parameter(s) fun : Callable, optional function to minimize, by default None @@ -359,7 +359,7 @@ def tf_rmsprop( @algo_reg_deco def tf_adadelta( - x_init: np.array, + x_init: np.ndarray, fun: Callable = None, fun_grad: Callable = None, grad_lookup: Callable = None, @@ -370,7 +370,7 @@ def tf_adadelta( Parameters ---------- - x_init : np.array + x_init : np.ndarray starting value of parameter(s) fun : Callable, optional function to minimize, by default None @@ -487,7 +487,7 @@ def cmaes(x_init, fun=None, fun_grad=None, grad_lookup=None, options={}): Returns ------- - np.array + np.ndarray Parameters of the best point. """ if "noise" in options: diff --git a/c3/libraries/chip.py b/c3/libraries/chip.py index 755e20eb..65db1eea 100755 --- a/c3/libraries/chip.py +++ b/c3/libraries/chip.py @@ -249,7 +249,7 @@ class Resonator(PhysicalComponent): Parameters ---------- - freq: np.float64 + freq: Quantity frequency of the resonator """ @@ -294,11 +294,11 @@ class Transmon(PhysicalComponent): Parameters ---------- - freq: np.float64 + freq: Quantity base frequency of the Transmon - phi_0: np.float64 + phi_0: Quantity half period of the phase dependant function - phi: np.float64 + phi: Quantity flux position """ @@ -569,10 +569,10 @@ def __init__( phi_0: Quantity = None, gamma: Quantity = None, d: Quantity = None, - t1: np.float64 = None, - t2star: np.float64 = None, - temp: np.float64 = None, - anhar: np.float64 = None, + t1: Quantity = None, + t2star: Quantity = None, + temp: Quantity = None, + anhar: Quantity = None, params=None, ): super().__init__( @@ -687,7 +687,7 @@ def __init__( t1: Quantity = None, t2star: Quantity = None, temp: Quantity = None, - anhar: np.float64 = None, + anhar: Quantity = None, params=dict(), resolution=None, ): @@ -942,9 +942,9 @@ def __init__( phi: Quantity = None, phi_0: Quantity = None, gamma: Quantity = None, - t1: np.float64 = None, - t2star: np.float64 = None, - temp: np.float64 = None, + t1: Quantity = None, + t2star: Quantity = None, + temp: Quantity = None, params=None, ): super().__init__( @@ -1024,17 +1024,17 @@ class SNAIL(Qubit): Reference: https://arxiv.org/pdf/1702.00869.pdf Parameters ---------- - freq: np.float64 + freq: Quantity frequency of the qubit - anhar: np.float64 + anhar: Quantity anharmonicity of the qubit. defined as w01 - w12 - beta: np.float64 + beta: Quantity third order non_linearity of the qubit. - t1: np.float64 + t1: Quantity t1, the time decay of the qubit due to dissipation - t2star: np.float64 + t2star: Quantity t2star, the time decay of the qubit due to pure dephasing - temp: np.float64 + temp: Quantity temperature of the qubit, used to determine the Boltzmann distribution of energy level populations Class is mostly an exact copy of the Qubit class. The only difference is the added third order non linearity with a prefactor beta. @@ -1048,12 +1048,12 @@ def __init__( desc: str = " ", comment: str = " ", hilbert_dim: int = 4, - freq: np.float64 = None, - anhar: np.float64 = None, - beta: np.float64 = None, - t1: np.float64 = None, - t2star: np.float64 = None, - temp: np.float64 = None, + freq: Quantity = None, + anhar: Quantity = None, + beta: Quantity = None, + t1: Quantity = None, + t2star: Quantity = None, + temp: Quantity = None, params: dict = None, ): super().__init__( diff --git a/c3/libraries/fidelities.py b/c3/libraries/fidelities.py index 04382618..4ed2312b 100755 --- a/c3/libraries/fidelities.py +++ b/c3/libraries/fidelities.py @@ -117,14 +117,14 @@ def state_transfer_infid_set( @fid_reg_deco @state_deco -def state_transfer_infid(ideal: np.array, actual: tf.constant, index, dims, psi_0): +def state_transfer_infid(ideal: np.ndarray, actual: tf.constant, index, dims, psi_0): """ Single gate state transfer infidelity. The dimensions of psi_0 and ideal need to be compatible and index and dims need to project actual to these same dimensions. Parameters ---------- - ideal: np.array + ideal: np.ndarray Contains ideal unitary representations of the gate actual: tf.Tensor Contains actual unitary representations of the gate @@ -152,16 +152,16 @@ def state_transfer_infid(ideal: np.array, actual: tf.constant, index, dims, psi_ @fid_reg_deco @unitary_deco def unitary_infid( - ideal: np.array, actual: tf.Tensor, index: List[int] = None, dims=None + ideal: np.ndarray, actual: tf.Tensor, index: List[int] = None, dims=None ) -> tf.Tensor: """ Unitary overlap between ideal and actually performed gate. Parameters ---------- - ideal : np.array + ideal : np.ndarray Ideal or goal unitary representation of the gate. - actual : np.array + actual : np.ndarray Actual, physical unitary representation of the gate. index : List[int] Index of the qubit(s) in the Hilbert space to be evaluated @@ -221,14 +221,14 @@ def unitary_infid_set(propagators: dict, instructions: dict, index, dims, n_eval @fid_reg_deco @open_system_deco def lindbladian_unitary_infid( - ideal: np.array, actual: tf.constant, index=[0], dims=[2] + ideal: np.ndarray, actual: tf.constant, index=[0], dims=[2] ) -> tf.constant: """ Variant of the unitary fidelity for the Lindbladian propagator. Parameters ---------- - ideal: np.array + ideal: np.ndarray Contains ideal unitary representations of the gate actual: tf.Tensor Contains actual unitary representations of the gate @@ -288,7 +288,7 @@ def lindbladian_unitary_infid_set( @fid_reg_deco @open_system_deco def average_infid( - ideal: np.array, actual: tf.Tensor, index: List[int] = [0], dims=[2] + ideal: np.ndarray, actual: tf.Tensor, index: List[int] = [0], dims=[2] ) -> tf.constant: """ Average fidelity uses the Pauli basis to compare. Thus, perfect gates are @@ -296,7 +296,7 @@ def average_infid( Parameters ---------- - ideal: np.array + ideal: np.ndarray Contains ideal unitary representations of the gate actual: tf.Tensor Contains actual unitary representations of the gate @@ -377,7 +377,7 @@ def average_infid_seq(propagators: dict, instructions: dict, index, dims, n_eval @fid_reg_deco @open_system_deco def lindbladian_average_infid( - ideal: np.array, actual: tf.constant, index=[0], dims=[2] + ideal: np.ndarray, actual: tf.constant, index=[0], dims=[2] ) -> tf.constant: """ Average fidelity uses the Pauli basis to compare. Thus, perfect gates are @@ -385,7 +385,7 @@ def lindbladian_average_infid( Parameters ---------- - ideal: np.array + ideal: np.ndarray Contains ideal unitary representations of the gate actual: tf.Tensor Contains actual unitary representations of the gate diff --git a/c3/libraries/propagation.py b/c3/libraries/propagation.py index 795209fe..57b764db 100644 --- a/c3/libraries/propagation.py +++ b/c3/libraries/propagation.py @@ -37,7 +37,7 @@ def state_deco(func): @unitary_deco -def gen_dUs_RK4(h, dt, dim=None): +def gen_dus_rk4(h, dt, dim=None): dUs = [] dU = [] if dim is None: @@ -45,12 +45,12 @@ def gen_dUs_RK4(h, dt, dim=None): dim = tot_dim[1] for jj in range(0, len(h) - 2, 2): - dU = gen_dU_rk4(h[jj : jj + 3], dt, dim) + dU = gen_du_rk4(h[jj : jj + 3], dt, dim) dUs.append(dU) return dUs -def gen_dU_rk4(h, dt, dim): +def gen_du_rk4(h, dt, dim): temp = [] for ii in range(dim): psi = tf.one_hot(ii, dim, dtype=tf.complex128) @@ -69,7 +69,17 @@ def rk4_step(h, psi, dt): return psi -def get_Hs_of_t_ts( +def get_hs_of_t_ts( + model: Model, gen: Generator, instr: Instruction, prop_res=1 +) -> Dict: + if model.controllability: + hs_of_ts = _get_hs_of_t_ts_controlled(model, gen, instr, prop_res) + else: + hs_of_ts = _get_hs_of_t_ts(model, gen, instr, prop_res) + return hs_of_ts + + +def _get_hs_of_t_ts_controlled( model: Model, gen: Generator, instr: Instruction, prop_res=1 ) -> Dict: """ @@ -100,31 +110,64 @@ def get_Hs_of_t_ts( ts = [] gen.resolution = prop_res * gen.resolution signal = gen.generate_signals(instr) - if model.controllability: - h0, hctrls = model.get_Hamiltonians() - signals = [] - hks = [] - for key in signal: - signals.append(signal[key]["values"]) - ts = signal[key]["ts"] - hks.append(hctrls[key]) - cflds = tf.cast(signals, tf.complex128) - hks = tf.cast(hks, tf.complex128) - for ii in range(cflds[0].shape[0]): - cf_t = [] - for fields in cflds: - cf_t.append(tf.cast(fields[ii], tf.complex128)) - Hs.append(sum_h0_hks(h0, hks, cf_t)) - else: - Hs = model.get_Hamiltonian(signal) - ts_list = [sig["ts"][1:] for sig in signal.values()] - ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) - signals = None - hks = None - assert np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])) - assert np.all( - tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) - ) + h0, hctrls = model.get_Hamiltonians() + signals = [] + hks = [] + for key in signal: + signals.append(signal[key]["values"]) + ts = signal[key]["ts"] + hks.append(hctrls[key]) + cflds = tf.cast(signals, tf.complex128) + hks = tf.cast(hks, tf.complex128) + for ii in range(cflds[0].shape[0]): + cf_t = [] + for fields in cflds: + cf_t.append(tf.cast(fields[ii], tf.complex128)) + Hs.append(sum_h0_hks(h0, hks, cf_t)) + + dt = tf.constant(ts[1 * prop_res].numpy() - ts[0].numpy(), dtype=tf.complex128) + return {"Hs": Hs, "ts": ts[::prop_res], "dt": dt} + + +def _get_hs_of_t_ts( + model: Model, gen: Generator, instr: Instruction, prop_res=1 +) -> Dict: + """ + Return a Dict containing: + + - a list of + + H(t) = H_0 + sum_k c_k H_k. + + - time slices ts + + - timestep dt + + Parameters + ---------- + prop_res : tf.float + resolution required by the propagation method + h0 : tf.tensor + Drift Hamiltonian. + hks : list of tf.tensor + List of control Hamiltonians. + cflds_t : array of tf.float + Vector of control field values at time t. + ts : float + Length of one time slice. + """ + Hs = [] + ts = [] + gen.resolution = prop_res * gen.resolution + signal = gen.generate_signals(instr) + Hs = model.get_Hamiltonian(signal) + ts_list = [sig["ts"][1:] for sig in signal.values()] + ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) + if not np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])): + raise Exception("C3Error:Something with the times happend.") + if not np.all(tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0])): + raise Exception("C3Error:Something with the times happend.") + dt = tf.constant(ts[1 * prop_res].numpy() - ts[0].numpy(), dtype=tf.complex128) return {"Hs": Hs, "ts": ts[::prop_res], "dt": dt} @@ -150,14 +193,14 @@ def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Di Hs = [] ts = [] dUs = [] - dict_vals = get_Hs_of_t_ts(model, gen, instr, prop_res) + dict_vals = get_hs_of_t_ts(model, gen, instr, prop_res) Hs = dict_vals["Hs"] ts = dict_vals["ts"] dt = dict_vals["dt"] - dUs = gen_dUs_RK4(Hs, dt, dim) + dUs = gen_dus_rk4(Hs, dt, dim) - U = gen_U_RK4(Hs, dt, dim) + U = gen_u_rk4(Hs, dt, dim) if model.max_excitations: U = model.blowup_excitations(U) @@ -166,7 +209,7 @@ def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Di return {"U": U, "dUs": dUs, "ts": ts} -def gen_U_RK4(h, dt, dim): +def gen_u_rk4(h, dt, dim): U = [] for ii in range(dim): psi = tf.one_hot(ii, dim, dtype=tf.complex128) @@ -213,12 +256,15 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: h0 = model.get_Hamiltonian(signal) ts_list = [sig["ts"][1:] for sig in signal.values()] ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) - signals = None - hks = None - assert np.all(tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0])) - assert np.all( + if not np.all( + tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0]) + ): + raise Exception("C3Error:Something with the times happend.") + if not np.all( tf.math.reduce_variance(ts[1:] - ts[:-1]) < 1e-5 * (ts[1] - ts[0]) - ) + ): + raise Exception("C3Error:Something with the times happend.") + dt = tf.constant(ts[1].numpy() - ts[0].numpy(), dtype=tf.complex128) batch_size = tf.constant(len(h0), tf.int32) @@ -234,32 +280,6 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: return {"U": U, "dUs": dUs, "ts": ts} -# @state_deco -# def rk4(model: Model, gen: Generator, instr: Instruction, init_state=None) -> Dict[str, List[tf.Tensor]]: -# if init_state is None: -# init_state = model.get_ground_state() - -# key = list(signal.keys())[0] -# ts = signal[key]["ts"] -# dt = ts[1] - ts[0] - -# states = [init_state] -# for i in range(len(ts)): -# signals = [] -# for key in signal: -# signals.append(signal[key]["values"][i]) -# states.append(rk4_solver_step(model, signals, dt, states[-1])) -# return {"states": states} - - -# def rk4_solver_step(model: Model, signal, dt: float, state) -> tf.Tensor: -# k1 = model.eom(state, signal) -# k2 = model.eom(state + dt * k1 / 2.0, signal) -# k3 = model.eom(state + dt * k2 / 2.0, signal) -# k4 = model.eom(state + dt * k3, signal) -# return state + dt * (k1 + 2 * k2 + 2 * k3 + k4) / 6.0 - - #################### # HELPER FUNCTIONS # #################### diff --git a/test/test_two_qubits.py b/test/test_two_qubits.py index f0e88181..72f09218 100644 --- a/test/test_two_qubits.py +++ b/test/test_two_qubits.py @@ -24,6 +24,7 @@ # Libs and helpers import c3.libraries.algorithms as algorithms +from c3.libraries.propagation import rk4 import c3.libraries.chip as chip import c3.libraries.envelopes as envelopes import c3.libraries.fidelities as fidelities @@ -423,3 +424,9 @@ def test_optim_lbfgs_grad_free() -> None: lbfgs_grad_free_opt.optimize_controls() assert lbfgs_grad_free_opt.current_best_goal < 0.01 + + +def test_rk4() -> None: + """Testing that RK4 exists and runs.""" + exp.set_prop_method(rk4) + exp.propagation(model, generator, pmap.instructions["rx90p[0]"]) From 9acbafc56b4563da7c8703aa3bb0c9f9ee65305c Mon Sep 17 00:00:00 2001 From: frosati1 Date: Tue, 21 Dec 2021 17:08:00 +0100 Subject: [PATCH 72/80] Fixed type. --- c3/experiment.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/c3/experiment.py b/c3/experiment.py index 7dc14144..f2c926a0 100755 --- a/c3/experiment.py +++ b/c3/experiment.py @@ -372,7 +372,7 @@ def process(self, populations, labels=None): populations_final.append(pops) return populations_final, populations_no_rescale - def get_perfect_gates(self, gate_keys: list = None) -> Dict[str, np.array]: + def get_perfect_gates(self, gate_keys: list = None) -> Dict[str, np.ndarray]: """Return a perfect gateset for the gate_keys. Parameters From a72d019ed86bc8da69739043819bd2f13f13f02f Mon Sep 17 00:00:00 2001 From: frosati1 Date: Tue, 21 Dec 2021 17:14:35 +0100 Subject: [PATCH 73/80] hks signals restored --- c3/libraries/propagation.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/c3/libraries/propagation.py b/c3/libraries/propagation.py index 57b764db..b753b61f 100644 --- a/c3/libraries/propagation.py +++ b/c3/libraries/propagation.py @@ -256,6 +256,8 @@ def pwc(model: Model, gen: Generator, instr: Instruction) -> Dict: h0 = model.get_Hamiltonian(signal) ts_list = [sig["ts"][1:] for sig in signal.values()] ts = tf.constant(tf.math.reduce_mean(ts_list, axis=0)) + hks = None + signals = None if not np.all( tf.math.reduce_variance(ts_list, axis=0) < 1e-5 * (ts[1] - ts[0]) ): From e7c0690f413a255147967b69c3db7b2d35593605 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 22 Dec 2021 16:53:31 +0100 Subject: [PATCH 74/80] Updated signal chains to new format. --- test/c2_blackbox_exp.hjson | 16 ++++++++-------- test/one_qubit.hjson | 16 ++++++++-------- test/test_transmon_expanded.py | 4 ++-- 3 files changed, 18 insertions(+), 18 deletions(-) diff --git a/test/c2_blackbox_exp.hjson b/test/c2_blackbox_exp.hjson index 4d48a037..faf66e9f 100644 --- a/test/c2_blackbox_exp.hjson +++ b/test/c2_blackbox_exp.hjson @@ -598,14 +598,14 @@ Chains: { d1: - [ - LO - AWG - DigitalToAnalog - Response - Mixer - VoltsToHertz - ] + { + LO: [] + AWG: [] + DigitalToAnalog: ["AWG"] + Response: ["DigitalToAnalog"] + Mixer: ["LO", "Response"] + VoltsToHertz: ["Mixer"] + } } } } \ No newline at end of file diff --git a/test/one_qubit.hjson b/test/one_qubit.hjson index cf52d44c..0b575fab 100644 --- a/test/one_qubit.hjson +++ b/test/one_qubit.hjson @@ -200,14 +200,14 @@ Chains: { d1: - [ - lo - awg - dac - resp - mixer - v2hz - ] + { + "lo": [], + "awg": [], + "dac": ["awg"], + "resp": ["dac"], + "mixer": ["lo", "resp"], + v2hz: ["mixer"] + } } } } diff --git a/test/test_transmon_expanded.py b/test/test_transmon_expanded.py index 678e7146..b35d46c2 100644 --- a/test/test_transmon_expanded.py +++ b/test/test_transmon_expanded.py @@ -97,14 +97,14 @@ "awg": [], "dac": ["awg"], "resp": ["dac"], - "mixer": ["lo", "awg"], + "mixer": ["lo", "resp"], }, "Qubit2": { "lo": [], "awg": [], "dac": ["awg"], "resp": ["dac"], - "mixer": ["lo", "awg"], + "mixer": ["lo", "resp"], }, }, ) From 83612c4d23dcabda9c0b24da95a89d66782d99b0 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Wed, 22 Dec 2021 17:06:29 +0100 Subject: [PATCH 75/80] Added check for resolutions of multiple inputs. --- c3/generator/generator.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/c3/generator/generator.py b/c3/generator/generator.py index 9e955a78..b0f27f20 100755 --- a/c3/generator/generator.py +++ b/c3/generator/generator.py @@ -56,10 +56,16 @@ def __check_signal_chains(self) -> None: for channel, chain in self.chains.items(): signals = 0 for device_id, sources in chain.items(): - # all source devices need to exist + # all source devices need to exist and have the same resolution + if sources: + res = self.devices[sources[0]].resolution for dev in sources: if dev not in self.devices: raise Exception(f"C3:Error: device {dev} not found.") + if res != self.devices[dev].resolution: + raise Exception( + f"C3:Error: Different resolution of inputs in {channel} {device_id}:{sources}." + ) # the expected number of inputs must match the connected devices if self.devices[device_id].inputs != len(sources): From 77572352fa65a0511eb1cc26b3a87cc1b3fbcbf5 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler Date: Thu, 23 Dec 2021 13:21:55 +0100 Subject: [PATCH 76/80] Chains for entangling example updated. --- examples/two_qubit_entangling_gate.ipynb | 92 +++++++++++++++--------- 1 file changed, 59 insertions(+), 33 deletions(-) diff --git a/examples/two_qubit_entangling_gate.ipynb b/examples/two_qubit_entangling_gate.ipynb index 86d5947c..8093f314 100644 --- a/examples/two_qubit_entangling_gate.ipynb +++ b/examples/two_qubit_entangling_gate.ipynb @@ -311,8 +311,22 @@ " \"VoltsToHertz\": v_to_hz\n", " },\n", " chains={\n", - " \"d1\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"],\n", - " \"d2\": [\"LO\", \"AWG\", \"DigitalToAnalog\", \"Response\", \"Mixer\", \"VoltsToHertz\"]\n", + " \"d1\": {\n", + " \"LO\": [],\n", + " \"AWG\": [],\n", + " \"DigitalToAnalog\": [\"AWG\"],\n", + " \"Response\": [\"DigitalToAnalog\"],\n", + " \"Mixer\": [\"LO\", \"Response\"],\n", + " \"VoltsToHertz\": [\"Mixer\"],\n", + " },\n", + " \"d2\": {\n", + " \"LO\": [],\n", + " \"AWG\": [],\n", + " \"DigitalToAnalog\": [\"AWG\"],\n", + " \"Response\": [\"DigitalToAnalog\"],\n", + " \"Mixer\": [\"LO\", \"Response\"],\n", + " \"VoltsToHertz\": [\"Mixer\"],\n", + " }\n", " }\n", " )" ] @@ -595,8 +609,10 @@ "outputs": [ { "data": { - "text/plain": "

", - "image/png": 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\n" + "image/png": 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", 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\n" 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", 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\n" + "image/png": 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BVhe920VblrOOnfWGOF5HbRv2NKY+Ko0mS57c929IQVNLTmlvEBrqfPrGsrIyRo0ahdGoxMj6/PPPSUxMJDExkc8//7zO4/Pz8xk3bhyJiYmMGzeOM2eU/AOLFi3iySefdFouWwLe8K9du9bbIvgM5+risYlK7JNJbzVNHaltw57G1Mctn663LF/Ut22jnbe+FBcXO33sp59+yhVXXIFWqyU/P59nnnmG9PR01q9fzzPPPGMx5DUxa9Ysxo4dy969exk7diyzZs0CYNKkSfzyyy+UlpY6LVsVHjX8QoiJQojdQoh9QojHHezvKIRYIYTYLITYKoS42JPyqNhzvc2Y6ktLduEPIbp9joOr4YvLoCTP25L4DQdPl/D3fkVfax4d42Vp3M/XX3/NZZddBsDSpUsZN24csbGxxMTEMG7cOJYsWVLr8QsWLOCWW24B4JZbbuHnn38GlCGd0aNHs2jRIpdl9NhAlhBCC7wLjANygA1CiIVSSttB5ZnAXCnl+0KI3sBiIMGdcqgue1bO1UWz8CB6toli14ki3l+5n/dX7mfKgPY8fWkSzcIbf5p8Y+Ny28jbD59fqiz/rws8fhhCvet/7gqNda9c9o7yZDF1YDwdYsM9dp5nfslixzHnXDKNRiNabXVvt97tonnq0qQaj9Pr9Rw4cICEhAQAjh49SocOVk+l+Ph4jh6tfdZ8bm4ubdsqT0Ft2rSxCx+RmprKmjVruPrqqxvy51TDkz3+wcA+KeUBKaUe+A647JwyEqgabG4GHHO3EMOGNV0/9XNxpIsF951vt/7T5qMkP/s718xOayyxvIbLbePtFPv1WR1B7/pjuLdojHtl5/FCCssNALx0ZT+Pn89ZHBn9+nD69GmaN2/uNjmEEHYvb1u1asWxY66bSU++um4P2Aa8yAGGnFPmaeB3IcT9QARwoaOKjh8/To8eSk7ZkJAQpk2bxoABAwCIi4sjKSnJkhxZp9MxfPhwNm3aRGFhIaWlpYwcOZLc3FxL/I3ExERCQkLYvn07oCize/fuljHOkJAQhg0bRkZGhmWsb8iQIeTk5Fh+rXv06IFWq2XHDuUBpk2bNnTu3Jm0NMVghoWFMWTIENLT0ykrU2bIDhs2jIMHD3LihOLN0Lt3b4xGI7t3K0HT2rdvT3x8POnpSgzyyMhIUlNTSUtLo6KiAoDhw4ezZ88eTp48CUCfPn2oqKhg7969AHTo0IHWrVtTlbEsOjqalJQU1q5dS2FhIeHh4YwcOZKsrCzy8pTH7aX3pjLhPfsMZ+kH87nr/aVc1yuEmJgYkpOTWbVqFVIq8fxHjRpFZmamZbwyJSWF/Px8srOzAejSpQtRUVFkZmbW6zqB0ptpzOtUWlpKXFycU9epY8VOHPaPX2jLytELXLpOBoNiGM+9TsnJyRQVFXHgwAEAEhISiI2NtSQNcfU6lZaW0qFDB49dp/QNG7jmB0UfV6W048D+fR65n4qKigB4bFxXgoODKSkpAUCj0RAREUFxcbFlWDMyMpLy8nKLzkNDQ5FSUlZWhkajISgoiKCgIMu4ukaj9JWrzlFVR1lZGUajEYPBQHl5ORUVFej1emJjY/n7778xGo2UlpZy8OBBRo4cWa2OqKgoSktLMRqNtGzZkpycHFq2bMnhw4dp0aIFFRUVaLVazpw5g06no7S0lLCwMEu7r3LnnDlzJvPmzauqtoWjJgooEwA88QGmAh/brN8EvHNOmYeAh83Lw4AdgObcugYOHCidZcWKFU4fG2jUpYsyvUF+kZYtOz22yPLJL65oHOG8QK36OLlLyhUvSlmSV33fsS1SPhVd8+dYpsdk9iSevlf6PLXE0q7K9AaPnGPHjh1uqaewsNDpY+Pj42VZWZmUUsq8vDyZkJAg8/PzZX5+vkxISJB5eUqbuummm2R6enq14x955BH54osvSimlfPHFF+WMGTMs+1555RXLPlsc/d1AhqzBPntyqOcoYDsNL968zZbbgbkAUso0IJTafqVUPEpokJabhnYie9YkokKUh8E3lu3xslReoKII3h0MK1+ElzvD083gz+dBSuUl7uyRtR8/e0T1bZ9PVuo5nO4ZmX2cN5ftpcg8xPPeDSmEBgXubPHx48dbnnZiY2P5v//7PwYNGsSgQYN48skniY2NBWDr1q20a9eu2vGPP/44f/zxB4mJiSxbtozHH7f6xaxYsYJJkya5LKMnDf8GIFEI0VkIEQxcCyw8p8xhYCyAEKIXiuE/5U4hhgw5d3Sp6dIQXax5TPG2+DztkKfE8To16uPHu6pvW/0yLHoQPhxdfd+T+dW3nbXp4yx7Bg6uUpY/HQ+VrsWR8RSeulcKSvW8bu5AXJrcjot90H3zXCIiIpw+dvr06Xb++rfddhv79u1j3759TJs2DVBSOyYmJhIfH1/t+Li4OJYvX87evXtZtmyZ5YciNzeXsrIy+vbt67RsVXjM8EspDcB9wFJgJ4r3TpYQ4lkhxGRzsYeBO4UQmcC3wK3mRxS3kZOT487q/JqG6KJ5eDAhOqV5LLWZYRlIONRHZTns/tXxARvnwNlzQlzc9DNotPDYOT+Qr/dWevhPN4O1r9nv+2C4syJ7FE/cK1JK+j/7h2X97esGuP0cnkCv1zt9bEpKCmPGjLFM4HJEdHS07Vh8vTh8+DCvvvqq03LZ4lE/finlYilldyllVynl8+ZtT0opF5qXd0gpz5dSJksp+0spf3e3DHW5TjUlGqqL925QvFb++2tghnVwqI+t3zeskq5mP/Sw5nDH8lqLWsjbCwW+l+jDE/fKI/O2WpZXz/Afn/3KykqXjr/tttuc9gyqiUGDBtG/f3+31BXwM3dVnGdsr9YAHMkvo9Jo8rI0jYBBD7/8s/7l/33cfj2+AXla3+hT/7J+ytwNR/hhk/IUcfeornSM85zPvkrDCHjDX+UGquKcLkZ1bwnAN+mBF8Wzmj5+vNN+/emz8MBW6Hhe9YMfOwTBDgzZ5LdrPmG/a+3Xj/tWFjR33ismk+TRH5TefliQlscm+td9GOgTPwPe8Lv7ccufcUYX947uCsBTC7PcLY7XsdOHyQQ7frauT/lQ+Y7pBLf9Bi1sDFfSFGVoxxG9L3e8/ZI3YMoH9tv2LHVY1Fu48155+899luXtz0zwuzDJ/iZvQwl4w181IUTFOV0M7hxrWT5b5tq4p69hp4/V/7Pf2fucSeb3/A39roEh98CVn9ZcaWg0jHvOftuEFyF1GggB0zdYt6943jnBPYS77hWD0WTx4ln64Ei0PhZ5sz64msHL1wl4w6/iGkIIi3fPliMF3hXGk6x8wbo84mEIOicsr1YHV3wIF80CTR23zfn/hAcyleGgp8/CsHut+1p2hw42bpOmmj0//JXZqw9Ylnu08f0sVu7G1bDM8+bNIykpCY1GY5nZDbBt2zZuvfVWt8gY8Ia/TZs23hbBZ3BWFz/co4xxP/HD1jpK+hcWfZzYbr9jrBtinsck1DwcdPUX1uVVL7t+LjfhjnulvNLI/5YqoS0W3e+bbqv1wZVELK6GZe7Tpw8//vijJbRDFX379iUnJ4fDh11/3xbwhr8heSgDHWd10dPcazt2NrAef7tE6WHBffCBTaC6ibM8f+LI1tblVY1wvnrijnvlxcU7Lct92jfNSKWuhmXu1atXjS/aL730Ur777junZavCf/KLOUlaWpqaUNuMs7rQaTV0jA3ncH4pC7Yc5bL+7d0vXGNRWQ4562HNqwQfWFl9/+B/eF4GIaD7RNhjNgB5+yGuq+fPWwfuuFeqZnov/qeDsBWNzW+Pw4ltTh1qMhrQaB2YxzZ9leG+GnBHWObaSE1NZdasWTz66KNO1wFNoMev4h4mJysxRR74bot3BXEFkxGeb63E0Hdk9HtMUmbhNgbD/2VdfjsFDvl/GOy5GdZJabapPZsS7g7LfC7+EJbZJwgLC/O2CD6DK7p44MJE3lmhuOj9nnWC8Ul++O5k2/za949/rvb97qTjUPv1zybCpFfh14eV9bvWQNvGjVfv6r3y6HzlHdA3d/hIfKxaeuZ1UV5cTGRkZIOPCwsLs/MIat++PStXrrSs5+TkuPRUVV5e7habFvA9fjVImxVXdBGk1RBpjtj5jy83cjjPDxOO/FTHME5jD7eIc26/KqMPSoRPg/PxYpzBlfZh2x7O6+b/AXadMfqg5EQwGo0W4z9hwgR+//13zpw5w5kzZ/j999+ZMGECADfffDPr16+vrbpq7Nmzhz59XJ/1HfCGvyqpiYrruvjFxktj5P9WcCTfj4z/6b017+s+sXqQtcbg+rm17//DDd5FDcCV9nH/t0oymKoJf/6OK8nWXQ3L/NNPPxEfH09aWhqTJk2y/FCAf4Rl9gmqsvWouK6Lzi0ieGxiT8v6iJdXkPrfPyjT+4Ev+k93W5fH/Adu/IFVI+crfvbXf1+z66Un6eYw4ZyV9Peh/GzjyILz7aOgVE9mjiLnHSO6uFMkr+FKkGBXwzJPmTKFnJwcKioqyM3NZelSZYZ3RUUFGRkZXHTRRU7LVkXAG34V93LP6K70aW99cXe6WE+vJ2t3T/MJjtqklhz1KHS7EKnxckJ5IZQE7bXx/Y2NI4sL3PSJMlwRrNUQGxHsZWm8jyfDMs+aNculOQZVBLzhV5OtW3GXLn6693yuSLF36fxzV65b6vYIf71pXb7B+oLXJ9pGaDMlkcvAW5X1CS/A/Zus+w+ubjRRnNHHibPlbDuq9PYXP+C/E7bOxZVELOCZsMyJiYluc00PeMN/8OBBb4vgM7hLF0FaDa9d3Z+DL17MsC5xANw2J8Olx2OPIaX9WHniOMuiz7QNjRYufdMc3mG68pI55Wbr/l01JIZxM87oY+iLSg6CYK2Gbq0CJzxDRUWFt0XwKAFv+E+cCMzsUc7gbl0IIfjaxnVv14kit9bvFk7tsi4n2E8q8um2Mckma9evjzTKKRuqj6MF1ncCK2aMdrM03sVgMHhbBI8S8IZfxbNoNIKnLu0NwEVvrvGyNA6wjYB5xUfek6OhaIOgtdltr+iYEjbax5j4hjIM1ad9NO2bq/Nl/ImAN/y9e/f2tgg+g6d0cc0g65T0Ur2P9ZR2/mJdjrZP8u3zbeOqOdblYs8/nTREH2n78ygqV67117cPraO0/xEaGlp3IT8m4A1/bW/Wmxqe0kV4sI5mYYqHzMrdpzxyDqfItUkeYxsH34zPt40WidblkztrLucm6quP4goD1320DoDurSNpFu5l7ygP4Mr7KlfDMs+YMYOePXvSr18/pkyZQkFBAaCGZW4Qu3fv9rYIPoMndfHDPYpHyP3fbvbYORrMksety3Hdqu32i7bR8xLl+6srPH6q+urjmtnWuEI/3Xt+LSX9F1de7roalnncuHFs376drVu30r17d1588UVADcus4oNUeXQYTT7i2WMyWl0hm3eqO3mKr3L+g96WwI7s0yVkHSsE4KObU4kICfhwXw3G1bDM48ePt/jqDx06lJycHMs+NSxzPWnf3o9DCLsZT+vi5mGd+CLtEFtzCugX39yj56qTF2ymwt/gODibX7SNKJtgeNt/gD5XeuxU9dHHLZ8pk7UmJLVmXO/WdZT2Li+tf4ld+bvqLugAk8mExkFnoWdsTx4b/FiNx7k7LPOnn37KNddcY1lXwzLXE0dTopsqntZFt1ZKYKvnf/X8eHStfHcDGGySxrTs7rCYX7SN5lajwWLXbva6qEsfp4srOGQOxvb6Nf09Kou3cWT064M7wzI///zz6HQ6brjhBss2NSxzPUlPT1cTsZjxtC5uGNKJJxdkcarIi5NfjqyHXYus6/GDayzqN20jtDmUF0DpaSjKhSjP9LTr0se/vt8CwODOsYQH+77pqK1nXhdFRUVERTV8Qpq7wjLPmTOHRYsWsXz5coSwJqtXwzKr+BxajdJAD5wuoaTCC26dRSfgk3H2266Y3fhyuJvbllqXX3X89OJpDEYTa/aeBpSxfRXHuCMs85IlS3j55ZdZuHAh4eHhdvvUsMz1xNm42oFIY+hiVPeWAOw76XxYW6fI+hlePSdP6eXvQ2zN0SL9pm206mm/nu+ZUBO16eOhuZmAEqG1ynU3kHF2qAdcD8t83333UVRUxLhx4+jfvz93322NLOuusMzCJ+OrnENqaqrMyMiou6CK10k/kMc1Hyo+3tmzXG+g9aIot3pPeNyzcP4DjXP+xuDN/nDGxuA/VaBE92wEKgxGesxUPFFWzRhNpzjXAph5kp07d9KrVy+vyrBp0yZef/11vvzyyxrLFBYWcvvttzcoQmdFRQWjRo1i7dq11SJ0Ovq7hRAbpZQOH88Cvseflub/uUzdRWPoonNLq1FotE7F0ifs1+O61cvo+1XbuPWcQG3PNIenm0H+AbedoiZ9bDyk+J1Hhuh82ui7E1cSsahhmX2AQI+y1xAaQxetoqxT3ZfvPOnx82E0KG6OVcR2hen1S2fnV22jWXvofXn17W8NUHTgBmrSx/SvlTDRX/tKLt1GwNVOS0CEZRZChAghrhdC/FsI8WTVpx7HTRRC7BZC7BNCPF5DmauFEDuEEFlCiG8a+geo+B7XpCouiI/+sNXzJ8u2CQx3ww/wz01KmONA5NI3HW9fcK/HTmkySc6UVgLQp30zj51HpXGpb49/AXAZYABKbD41IoTQAu8CFwG9geuEEL3PKZMIPAGcL6VMAh5siPD1YfjwwEkO4SqNpYtnLksCIL9ET1F5pWdP9uXl1uXEOlIZnoPftY2w5jDxperbt34PRtf17Egfi7cfB+DS5HYWr62mgN+8+HeS+hr+eCnlNVLKl6WUr1Z96jhmMLBPSnlASqkHvkP58bDlTuBdKeUZACml28cG9uzZ4+4q/ZbG0kVokLXHffOn9Rt2cYq8/dblG36ouVwN+GXbGHo33PILDJ0Orftat393vctVO9LHm8uUJPV3jujscv3+hK0vfiBSX8P/txCib93F7GgPHLFZzzFvs6U70F0I8ZcQYp0QYmIDz1EnJ082wjizn9CYunjn+gEAbD5cQEGp3jMnWfpv63LXCxp8uN+2jc4jYeILcM9a67a9v0NZgUvVnquPSqOJvWa3XK+H4GhkAj0RS31fDw8HbhVCHAQqAAFIKWU/N5w/ERgNxAOrhRB9pZQFtoWOHz9Ojx6Kj3ZISAjTpk1jwADFsMTFxZGUlMTq1UpALp1Ox/Dhw9m0aROFhYUUFxdTXFxMbm4uR44ov0OJiYmEhISwfft2QJkG3b17d4vvbUhICMOGDSMjI8Pydn/IkCHk5ORY4mz06NEDrVbLjh07AGjTpg2dO3e2eEaEhYUxZMgQ0tPTKStTMhUNGzaMgwcPWjId9e7dG6PRaImK2L59e+Lj40lPTweUx83U1FTS0tIsL96GDx/Onj17LDdpnz59qKioYO9epWfWoUMHWrduTZX7a3R0NCkpKaxdu5bi4mJWrlzJyJEjycrKIi8vD4Dk5GSKioo4cEDxEElISCA2NpZNm5SXejExMSQnJ7Nq1SqklAghGDVqFJmZmZZIgykpKeTn55Odna3I1dnaQ+z/7B8svL4Dffr0qfE6gRKHpN7XSUpG71FcDE90mcouc70NuU7FxcWkp6f73HWqMjr1uU4tbvyNyK8uAuDAmnl0GX9ng65Tly5diIqKIjMzk+LiYrZt22a5nxbsU36wRyS2cP460fj3U1GRkgkuKCiI4OBgSkqUUWmNRkNERATFxcWWl7eRkZGUl5dbdB4aGoqU0lJPUFAQQUFBlJaW2tVRdY6qOsrKyuy8eC6++GJ++eUXtFotV155JevXr2fo0KHMmzcPrVZLeHi4XR1RUVGUlpZiNBqpqKjg3nvvZePGjcTExDBnzhwSExPZsWMHr7zyCh988AFarZawsDCLPqueUGbOnGnrLdSCmpBS1vkBOjn61HHMMGCpzfoTwBPnlPkAmGazvhwYdG5dAwcOlM5y6tQpp48NNBpbF++t2Cc7PbbI8jldVO6+ytPek/KpaOVT6Vy9AdM2qvTwVLRL1djqw2QyWa7b+oN5rkrYaOzYscMt9ej1eqePfeedd+Qbb7xhWV+2bJlcuHChnDRpUr2Of/fdd+Vdd90lpZTy22+/lVdffbVl39ixY+WhQ4eqHePo7wYyZA32uV5DPVLKQ0Bz4FLzp7l5W21sABKFEJ2FEMHAtcDCc8r8jNLbRwjRAmXox32OyfiZy56HaWxd3D7cflx44H+XUV7ppuQnae9Zl3UhTlURMG3jrtVuqcZWH5k5Zy3LgxJi3VK/PyFdcOe0DcsMMHbs2AbF/VmwYAG33HILAFOnTmX58uUWeRo1LLMQ4gGUF7E/mjd9JYT4UEr5dk3HSCkNQoj7gKWAFvhUSpklhHgW5ZdooXnfeCHEDsAIzJBS5rnw91Rj7969/hF+txFobF0E6zSsmjGaUf9badn20NwtvHfDQNcqNhnhrDkZxR3Lna4mYNpGqyTrck4GxDsXS8dWH1Pf/xuAGRN61HaIT3PihReo2OlcWGaD0YjOgR9+SK+etPn3vx0coXBuWGZnsA3lrNPpaNasGXl5ebRo0aLRwzLfDgyRUj4ppXwSGIryQ1ArUsrFUsruUsquUsrnzdueNBt9zE8kD0kpe0sp+0opXf8pU/EpOsVFcPDFi5mQpESUXLztBCfOuugxcSbbuuykkQsotDoIi1GWt851ubriCgMGc0Kdf4ysOdaRSnXcGZbZEY0dllmg9MirMJq3+Ty2SRCaOt7ShRCC2TelkvC4Enbgrq82smC6Cyn73lPSPNL3apfkCqi2cfkH8O01incPLztVRZU+Pl2rxARK7RRDkNZ/J/fX1jOvi/LycqcSrp8bltkZ2rdvz5EjR4iPj8dgMHD27Fni4uIscjVmWObPgHQhxNNCiKeBdcAnLp+9EWjd2rezBDUm3tbF+n+PBSDzSIHzY6gmExjNY9HJ19Retg68rQ+30sPsCW0byK2BtG7dGpNJ8tofij//S1NdddrzX4KCnItAem5Y5tp44okn+Omnn6ptnzx5siUp+/z587ngggssMfkbNSyzlPI1YBqQb/5Mk1K+4fLZGwE1qqcVb+uiVXQo8TFKb+Wt5fucq2SnjX9AwgiX5PG2PjxGwZG6yzggIyODr9dbE3l3bRnYs1dro8p90xlswzIDjBgxgquuuorly5cTHx/P0qVKfoVt27bRpk2basfffvvt5OXl0a1bN1577TVmzZpl2eeusMy1Gn4hRLT5OxbIBr4yfw6Zt6moNIh3rk8B4PVlTs6anXeLddlJb56AZYg5bvunE5w6vLRS8n8/K774bwR4akVPMn36dEuPHWDNmjWcOnWKsrIycnJyLIlYKisrGTZsWLXjQ0NDmTdvHvv27WP9+vV06aK8Z6moqCAjI4OLLrrIZRnrGuP/BrgE2AjYPpsL87rPv/mJjo72tgg+gy/oon+H5pblvOIK4iIbYLx32PT2b17gsiy+oA+30sycM7fwKEjZ4Hj9L2+0zrC+NLl6gpCmhCuJWGzDMtcWobOq519fGi0ss5TyEvN3ZyllF5tPZymlzxt9UC6CioKv6OKKFMVlcG9DsnSZjDD3Jut651Euy+Er+nAbKTZPQ0c3NujQ3MJysguU2avz7x7WpAKyOSIiwrW8A4ESlrmas7Sjbb6I7VhbU8dXdHHv6K4A3PxJAwK42Y7tD7nbLdmnfEUfbiPU5glm4T8bdOj1HylZ0+JjwkhtghO2zsU2nEIgUtcYf6h5LL+FECJGCBFr/iRQPeCaTxLowZYagq/oomOs0pvSG031O8BkhHm3WtcnvOAWOXxFH25l3LPK98ksxQOqHpRXGtl/Solns/gB116Yq/gHdfX470IZ3+9p/q76LADe8axoKoFKsE7DJf3aAnCysB4+zz/+w7o89bPATbTiDgZOsy6ve7deh9z/7WYA+rTQEh0a+InUVeoe439TStkZeMRmbL+zlDJZSukXhn/kyJHeFsFn8CVdxEYEA7Bo6/HaC2avhe3zretJU9wmgy/pw23YDvf8PrPO4gWlev7YkQvAJ3e6/t4kUFATsQBSyreFEH3MaRJvrvp4Wjh3kJWV5W0RfAZf0sUj5hgwy3fl1lxIXwJzbHyW/7HSLWP7VfiSPtzKlNnW5eO1p798eakSanpAx+acPOzk3IoApCr0s7PHjho1yhKmeeLEiTRv3pxLLrmkXsevXr2alJQUdDod8+dbOz2nTp1i4kT3pCyp78vdp4C3zZ8xKHPCJ7tFAg9TFctcxbd0UTWk8Ne+WmT66W7rcqfzod0At8rgS/pwK8nXWpdnj4BdixX3Tgd8k65M2Pr8tsGBqw8nsI2t31A+/fRTrrjiCotXz4wZM/jyyy/rfXzHjh2ZM2cO119vn1WtZcuWtG3blr/++stp2aqor7PqVGAscEJKOQ1IBtTMyyou0TJK8eE/VeQgPLK+1OrJEz8YblnUiJIFAF3GWJe/uw5+vqdakV8ylWBfCXHh6ti+G3E1LHNCQgL9+vVzOJfg8ssv5+uvv3ZZxvrOBCiTUpqEEAbzbN6TgF9EuEpOTva2CD6Dr+ninlFdeXbRDqZ/s4m5d50zg3Ht69blab+BCxNqasLX9OFWpnwAr9qEVM78FgbdYRfNdN7GHMA6mzrQ9LFm7h5OH2nAXBEbpDmD2bm06BDJiKu713icO8Iy10ZqaiozZ9b97qYu6ns3ZQghmgMfoXj1bALSXD57IxDo/rgNwdd0MbZXKwDWH8yvvnO1OcLkZe8pYYc9gK/pw61EtamegP7jscqTFIoL5+o9pwDo0155eA9ofTQQiXNBBAMqLLOU8l7z4gdCiCVAtJSy9rdGPsKBAwfo2LGjt8XwCXxNF53irLMjD+eV0jEuXFnJtplYNeAGj53f1/ThdhIvhP87DZ9dDDnmyXI/3w1Xf8GWIwUAXDvI+uAeaPqorWdeF0VFRQ0anqnCHWGZa6NRwjILIVLO/QCxgM68rKLiEgM7KQlEft5y1LqxypOn71VekCjA0AbB7b9DuDnv9o4FoC9lxvxMAG4c2smLwgUe7gjLXBuNFZb51Vo+r7h89kbAU2Nt/ogv6qIqCmRVDHhKTlt3TpxV/QA34ov68AhCwL+srqty+TMcyVfcFZPaWf3+m4w+6kFwcLDTx7oalnnDhg3Ex8czb9487rrrLpKSrKk13RWWudahHinlmNr2+wOxsWrckSp8URftmlsfW4/kl9Jh2UPKikYHES08em5f1IfHCAqFSa/Brw8h0j8ARtIxNtzuBWaT0kcduBIBc/r06bz++utceOGFgBKW2RE1hWUeNGgQOTk5Do9ZuHAhCxa4Hpm2vn78Nzv6uHz2RmDTpk3eFsFn8EVdaDWCnm2UsdTHftiqDEUA3LXa4+f2RX14lNTbLIsRlPHmtf3tdjc5fdSCK4lYbMMy10ZDwzKfOnWKhx56iJiYGKdlq6K+Xj2DbD4jgKfxkwlcKr7P29cpE7POHrAJJdw6qYbSKk4jBPRT0lXeol1qlxtBxb14Iixzy5Ytufzyy91SV329eu63XTe7dn7nFgk8jDt+HQMFX9VFYmulxz9csw0A2eUCGiMavK/qw5OsCLmAMXzPo0FzQXxkt68p6qMm3G20fQ1nZ8WUAJ3dKYinCLRJKa7gy7p49rIkngj6FoDRh251Phl7A/BlfXiKZ7JaWlcMert9TVEfNREeHu5tETxKfcf4fxFCLDR/fgV2Aw3zQ/ISq1at8rYIPoMv6+Km3taQAYdKdLyxbK/Hz+nL+vAEZXoj2fk2boaH7edgNjV91EagT2arb4//FaxunC8AI6WUj3tMKjfSGD1Hf8GXdSF+UTJGfWZQElG/uXwvp4sdxPBxI76sD09QFZvnh3YzlA2bv7Lb39T00ZSpb1jmVSi9/GYoE7j8JnWRo3gbTRWf1UVFEexbBsCND7xo2Zz632UePa3P6sNDPPqDMtl+4CV3Khu2zbXb39T04SlcDcv82muv0bt3b/r168fYsWM5dOgQ4J2wzHcA64ErUCJ1rhNC3Fb7Ub7BqFFqcokqfFYXK6ypFINadmXz/42zrG/LOeux0/qsPjzAsQJrfPmEdq2tOwqOWBabkj7qwplwDVW4GpZ5wIABZGRksHXrVqZOncqjjz4KeCcs8wxggJTyVinlLcBA4DGXz94IZGZmelsEn8EndWHQw7r3lOUrFC+TmIhgS0L2S9/xXEJ0n9SHh3jM3Nu/IsWcKru7uee46F+WMk1JH3Xhih+/q2GZx4wZY3m5PHToULvJXI0dljkPsH3bUWTe5vOcOXPG2yL4DD6pi63fW5d7X25ZfGhcd95buR9Q4vVXxe53Jz6pDw+QX6JnzV4lFMZLV/ZTNo59CvYsgX1/KElahAg4fayY8yEnDx1w6lijwYhWV92ls1WnLoy59R8OjlBwd1jmTz75hIsuusiy3thhmfcB6UKIp83ZuNYBe4QQDwkhHnJZCpWmy8L7lO+LXwGdNT6KTqvh4XFKdMUv07K9IFjgMPTF5QD0bBNFkNZ8y7fubS1Q6hd9OL/AnWGZv/rqKzIyMpgxY4ZlW6OGZQb2mz9VVAWLcH4grJFISVGDiFbhc7o4ssG6PPjOarvvGtWVV//Yw1t/7uOh8T2q7XcVn9OHB9h3sgi9wQTAl7cPsd/Z+3LY8TMsehCu+Srg9FFbz7wujEajU5O43BWWedmyZTz//POsWrWKkBDr0667wjLXd+buMwBCiEjzer3S2gghJgJvAlrgYymlw3CLQogrgfnAICllRn3qri/5+flER0fXXbAJ4FO6MBrgEyWIFQNudFgkWKdBCGUkosJgJMTBo7cr+JQ+PMRVHyi++veN6VZ9uGzYfYrh3/kL0DT0UV8MBoNTht82LHNoaGitZZ944gkGDx7MlClT7LZv3ryZu+66iyVLltCqVSu7fY0VlhkAIUQfIcRmIAvIEkJsFELUGkxFCKEF3gUuAnoD1wkhejsoFwU8AKQ3VPj6kJ2d7Ylq/RKf0sWC6dbl8c/XWOzZyUozW77zpNtF8Cl9eIAdxwo5U1oJwAMXJlYvYJOGkUN/B7w+GoJer6+7UA24GpZ5xowZFBcXc9VVV9G/f38mT7aGRWuUsMw2fAg8JKVcASCEGI2ShvG8Wo4ZDOyTUh4wH/MdcBmw45xyzwEvoXgOqTQFjm2GreZQTxNfgrDmNRbtG6/s+++iHVzct63nZQsgLn5LCQf88tR+1rF9W4SAuG6Qtw8+uwhGux7uV8X1sMzLltU8f8VdYZnra/gjqow+gJRypRAiorYDgPbAEZv1HMBukNGcxauDlPJXIUSNhv/48eP06KGM8YaEhDBt2jQGDFAiOsbFxZGUlMTq1UoYX51Ox/Dhw9m0aROFhYXo9XqKi4vJzc3lyBFFnMTEREJCQti+fTugvDDp3r275Vc6JCSEYcOGkZGRQXGxMqo1ZMgQcnJyOHpUyRTVo0cPtFotO3Yov2Nt2rShc+fOpKUpj9ZhYWEMGTKE9PR0ysoUH+phw4Zx8OBBTpw4AUDv3r0xGo3s3r1bUVj79sTHx5Oerjz8REZGkpqaSlpaGhUVyizW4cOHs2fPHk6eVHrAffr0oaKigr17lRAHHTp0oHXr1mRkKCNm0dHRpKSksHbtWvR6PStXrmTkyJFkZWWRl6e81EtOTqaoqIgDBxQPiISEBGJjYy1hemNiYkhOTmbVqlWWJNSjRo0iMzPT4gmSkpJCfn6+pdfYpUsXoqKiLC6Cttdp9Eqrq9um4MEUrlwJKB4L516n+DBleOLY2XKysrLo0aOH266TXq8nPT3d566TwaDMj3TlOmWerLTouEdQPivNHlLnXqduE+cQ//VwADrv/oBtddxPNV0nX7qfqsItBAUFERwcTElJCQAajYaIiAiKi4sts5QjIyMpLy+36Dw0NBQpJSaTiaKiIoKCgggKCrK4d1bVYRvSITIykrKyMsuErX79+jF8+HAKCgrQarUEBwej0+ksdWi1WsLDw5k/f76lnqioKEpLSy11hIeHYzAYLE8ewcHB5Ofnc88991jqCgsLs+iz6r3CzJkzmTdvXpVoNSa0EPWZpi2E+AklwXrVLIQbgYFSyim1HDMVmCilvMO8fhMwREp5n3ldA/wJ3CqlzBZCrAQecTTGn5qaKqtukIZy5swZNeqgGZ/QxaE0+MzsQ/6fExBU94uqQc8v41RRBQvvO59+5icAd+AT+vAAUko6P7EYgPl3DyM1oY4EK083sy6P/y+cd3/NZX2cnTt30qtXL5frMRgMLiVjaWwc/d1CiI1SylRH5evrznkb0BL4EfgB5Zekrpm7R4EONuvx5m1VRAF9gJVCiGxgKLBQCOFQUGdRJ6VY8QldVBn9S96ol9EHePpSZZz/7/3udTv0CX14gM3mROrNwoLqNvoA0228q36fCTsXeUYwP6LqqSJQqSvZeqgQ4kGUcfgslB77QCnlg1LKumZ7bAAShRCdhRDBwLXAwqqdUsqzUsoWUsoEKWUCytyAye726lHxIY5tti6n1D+B2+geSijhWb/tcrdEAcl/flKGXL64bXD9DmjZHW78wbr+/Q1w1nHqP5XAoK4e/+dAKrANxTvnf/WtWEppAO4DlgI7gblSyiwhxLNCiEbL3hUXF9dYp/J5vK6LD80pnCfOAk39XeUiQqyP3EaT+yJIel0fHqC80sjO48pYfL/4ZnWUtqHbhey6aL51/fUkMJncLJ3/0NQTsfSWUt4opZyNEpxtZEMql1IullJ2l1J2lVI+b972pJRyoYOyoz3R27fNUN/U8aouTu0BzEZ7UPXJWnVx3eCOALz+xx63iRSIbaPK7fXRiT0aHG2z+6CxcJeNB8q6d90pml/hjklSvkxdht/iGmDuwfsdVd4JKl7WxbfXKt8TXgBtw1+a3T5cSfiWdcx90ToDsW08PG8LALed3/AEeatXr4a2/eCS15UNv88EU+0JwwOVKm8ZZ7ANy7xlyxaGDRtGUlIS/fr14/vvv6/z+IqKCq655hq6devGkCFDLB5Y27Zt49Zbb3VaLlvqMvzJQohC86cI6Fe1LIQodIsEKoGPyQT55ogfw6bXXrYGurWKBGDF7lOWEAQq9hiMJsorTWg1gtAgF4YqUm38Nvb/6bpgTQzbsMzh4eF88cUXZGVlsWTJEh588EEKCgpqPf6TTz4hJiaGffv28a9//YvHHlMCIfft25ecnBwOHz7ssoy1Gn4ppVZKGW3+REkpdTbLfjG3259csjyN13Sx+mXl20U3wS4tlakje3LdkxYv0NrG+2Zf/Veu6ufU8Xb6uO135fv7m1wVq8lhG5a5e/fuJCYqs6bbtWtHq1atOHXqVK3HL1iwgFtuuQWAqVOnsnz5csu8g0svvZTvvvvOZRkDq+U7YPjw4d4WwWfwmi5WmrNqDXPN8P/38j5c/1E6l7y9luxZrk9bD7S28fHagwBc1Me5Gc52+uhonmtpKLOEbfY3Cn7Zj/5YidPHO3LoDG4XQfNLu9Z4TG1hmdevX49er6dr15qPBzh69CgdOiie8DqdjmbNmpGXl0eLFi1ITU1l1qxZluQszlJfP36/pWpWo4qXdJF/0Loc1brmcvXAdvJWeaXrY8+B1DZOnC3nbFkl0aE6p4d5quljlDmtdvoHLkrnf5icfLdRU1jm48ePc9NNN/HZZ5+h0Thvdhs7LLPfUjXNXMVLuvjhDuV76qcuVxUZoiNEp6HCYOKLtGz+MbL2nlNdBFLbmL1aGeZ5eapzwzzgQB/9roZVs2D9hzD0HlfE8wq19czroqioyKn0i47CMhcWFjJp0iSef/55hg4dWmcd7du358iRI8THx2MwGDh79qzF9dhdYZkDvsev4kXKCuCo2UO3h+tDMwCfTRsEwAuLd1FhaJoeJ45YvUcZNx6R2NJ9lcaZDWf+Aahw3sulKWEblhmUoZ8pU6Zw8803M3XqVLuyTzzxBD/99FO1OiZPnsznn38OwPz587ngggssrrmNGpbZn0lNdWsECL+m0XWx7Cnlu+9VEFR7bPL6cl5Xa9ypHjOXuFRXoLSNA6eK2X+qhKkD4+0muzUUh/oYeKvyvbVuN8RAoirnrTPYhmWeO3cuq1evZs6cOfTv35/+/fuzZcsWoOawzLfffjt5eXl069aN1157jVmzrGlM3BWWOeANf25urrdF8BkaVRcmE2ycoyxX+YW7iR/usUYD/+yvg7WUrJ1AaRurzL39MT1a1VGydhzqY7g5s+qvTSvDamVlZd2FamD69OmWHvuNN95IZWUlW7ZssXz69+9vOYejsMyhoaHMmzePffv2sX79erp06QIo/v0ZGRl2OXidJeANf1XoWJVG1sXaV5Xv+EEQ4t4MnQM7xXDLsE4APPPLDnYcc26sPlDaxjO/KKGML+5bvffYEBzqI6aTdbnktEv1+xOuGP6UlBTGjBljCbFcE1UJWerL4cOHmTVrllvckAPe8Kt4AYMe/vyvsjz1M4+c4unJ1nALF7+1hpIKv5xY7jK7Tig/eomtIhscoqHe9L7cfLJfPVN/AHLbbbe5Pd5PYmIio0ePdktdAW/4qyZPqDSiLv5+S/lulQTNO9Re1kmEEOx6bqJlPeW5PxpcRyC0jarYRTMvqZbVtMHUqI/L31e+f/mny+fwF2wTnAciAW/4A/0CNoRG0YW+BP58Tlm+YV7tZV0kNEhLxkwlvV2FwcTeBs7o9fe2cbKwnKVZyrj8yMQaky3Vmxr1EWzzorM03+Xz+AMee3ryEQLe8Felg1NpJF28f77yHdsFmrX3+OlaRIbw3OWKe9u/5m5p0LH+3jbu/VqZcHXv6K5uMVS16uPiV5Tvebe4fB5/4Fxf/EAj4A2/SiOy/084Y/ayub3mhNHu5sYhSsjm7UcLOVYQ2JmTqth1opCMQ0oupAcv7O75E/a7Wvk+uFp5qlPxawLe8Ldq5ZqLWyDhUV0Y9PClOQXzJW9AROMlORFC8MKUvgDMy6h/5ih/bhvXf6Qken/n+gEE69xzG9eqj9BmEGz2zvroArecz5dxxXPG1bDMq1evJiUlBZ1Ox/z51uQ4p06dYuLEibUcWX8C3vB3794IvSE/waO6WPG88h2XCKnTPHeeGrhmkPIS+fVl9U/U4q9t4+fNR8kv0QNwSb92bqu3Tn3c85fyfWoX/P5/bjuvLxIa6vyEQ1fDMnfs2JE5c+Zw/fXX221v2bIlbdu25a+//nJatioC3vBXzaBT8aAuygrgrzeU5Tsab4jHFq1GkNyhOQAHT9dvKMIf20ZReSUPfr8FgFUzRru17jr1EdMJBpjDNP/9Fhxe59bz+xKuJGJxNSxzQkIC/fr1cxjM7fLLL+frr792WrYqAj5Im0oj8OXlyveoxyCsudfEePDCRKZ9toE5fx3kmctcj2fii9z91UYAbhnWiU5xEY0vwOS3Iesn0BfDpxPg1l8hwTfDW//222+cOHHCqWONRqNDP/w2bdrUOnPWHWGZayM1NZWZM2c6fXwVAd/j93eXPXfiEV0c3wrHNivLY/7t/vobwOjuSoCyz9MOWRJX1Ia/tY2Nh/L5a18eAE9d6v58wfXShxDwuE0GqDmTlCBuKoAaltlncBQLo6niEV1Uuffd6v1ZnUIIEuLCyc4r5Uh+GR3jag+05U9tQ0rJDR8rL3RXzxiDRuN+P/N660OjhSfPwEsJUHEW3hoATxX4XLIWd8S0aSjuCMtcG2pY5nqSkZHhbRF8BrfrIneH0ttr2dNnHvffum4AAI/9sLXOsv7UNlbuPkV5pYnJye3q/EFzlgbpQ6OBx7KV2dkAa17xiEzeoqTEOZdVd4Rlrg01LHM9ceUlTaDhdl38bh5rvOYr99brAr3aKqmgN2TXPcPUn9rGs4t2EB6s5fkpnnt30WB9aDRwhzlUxp//DaiY/SaTyeljXQ3LvGHDBuLj45k3bx533XUXSUnWYT01LLOKdzmxDfYvh2YdoYXvxLwJ0moY1b0lBpO0BDDzd/afKubg6RLG9W5NVGiQt8WxJzgCRjysLFcF5mviuBqWedCgQeTk5FBSUkJeXh5ZWVmWfQsXLuTGG290WcaAN/xDhgzxtgg+g1t1MecS5dvD8Xic4ZbzlFDCdU3m8pe28aw57PKMCT08eh6n9THqMeU7/X0lMXsAEBHhvMeUp8Iynzp1ioceeoiYmBinZasi4A1/Tk79Z3IGOm7TxeF1UF6gjO236umeOt1IVUKST9bWnqTFH9pGXnEFq/acol98M+JjPDO2X4XT+tCFwMgZyvLehkdJ9UX0er1Lx3siLHPLli25/PLL3VJXwBv+o0ePelsEn8EtupBS8d8GuMb1iSSeQAhBSsfmAPy9r+bkIf7QNr5ap7hOPjrB8z+wLunjvPuV72+vcY8wXsaVRCz+QMAbfhU3U5WMo8fF0KKbd2WphUcnKobyerMLpD9iMkk+XnuAhLhwhrsh7LJHCW0GHYeBNMGZQ96WRqUOAt7w9+jh2XFRf8ItuvjhduV78tuu1+VBhnaxBonLqMHDx9fbxrqDeRSVG7h2cMdGOZ/L+hj7lPK9clbt5fwAf5vc11AC3vC7e5zNn3FZFxs/B0M59LsGIny8Bwr8+2Kl1z/1gzQqDNVftPl62/h+g5ID94oUz+c1ADfoo5PZQyXzG78P3awmYnEBIcREIcRuIcQ+IcTjDvY/JITYIYTYKoRYLoTo5KgeV9ixY4e7q/RbXNJF+Vlr6r1L3nCLPJ7m9uFdLMsXvLKq2n5fbhuniytYsOUYk/q1pVWU85EiG4Jb9DHmP8r3lm9cr8uLuJKIxdWwzK+99hq9e/emX79+jB07lkOHlKEzvwjLLITQAu8CFwG9geuEEOcmBt0MpEop+wHzgZc9JY+KC0gJryvx7rlgpn0qPh9GqxGseXQMAEcLyrj07bX1iuHjC7y9fC8AUwfGe1mSBjLkLuV78SMB49rZUFwNyzxgwAAyMjLYunUrU6dO5dFHHwX8JyzzYGCflPKAlFIPfAdcZltASrlCSllqXl0HuL2VO5oZ11RxWhcrnldisuhCYcQj7hXKw3SIDefDmwYCsO3oWXrMXILRpBgkX20bRpPk87RDBGkFoxJbNtp53aKP0GbQa7KyvHWu6/V5CVcSsbgalnnMmDGEhyudq6FDh9q52fpDWOb2wBGb9RygthkitwO/uVuIzp07u7tKv8UpXZzaDav/pyzP2Odzgbjqw/ikNsy9axhXz05DbzQx6a01LHlwpM+2jdf/UJLJPD05ySPB2GrCbfqY8DzsXAg//QN6XaLM7vUCe/Y8R1HxTucOltJhW4+K7EX37jUnoXF3WOZPPvnELticu8Iy+0R0TiHEjUAqMMrR/uPHj1s8DkJCQpg2bRoDBijBuOLi4khKSmL16tWA8ks9fPhwNm3aRGFhIcXFxYwePZrc3FyOHFF+hxITEwkJCbEkl27VqhXdu3e3xNcICQlh2LBhZGRkWOKXDBkyhJycHIuvc48ePdBqtZZx0TZt2tC5c2fS0tIAJUrfkCFDSE9Pp6xMyQM7bNgwDh48aIkR3rt3b4xGI7t37wagffv2xMfHk56uuCBGRkaSmppKWloaFRUVAAwfPpw9e/Zw8uRJAPr06UNFRQV79ypDAx06dKB169aWgFvR0dGkpKSwdu1aCgoKiIyMZOTIkWRlZZGXp4T4TU5OpqioiAMHlPC6CQkJxMbGsmljBoM2PEAEwD9WsmrdJqSUCCEYNWoUmZmZnDmj5H1NSUkhPz+f7OxsALp06UJUVBSZmZn1uk6gNGpPXafSQzuYfWE4dy0rZdeJIl7+bhm9I8tp2bKlT12nBX+s5p0VRQBcPTCebdu21X2dNilJ12NiYkhOTmbVqlVOXafi4mI6derkluvUq8MwxJE0eKEdaeN/Zdh5wxvtfioqUvRnMBpASoymqhf7Aq1Wa55Rqzz1abVaTCaTZQhQo9GABKPJhBAghAYhBCZzHZUGxb+/6hxV17+srAyj0cjx48dp3rw5FRUVlklgwcHBnDp1ihtuuIEPPviA8vJywsPD7eqIioqitLTUMtu3aogoPT2d3377jYqKCrRaLWFhYRw9epTS0lLCwsIs+qx6JzFz5kzmzbPMpq/ZA0NK6ZEPMAxYarP+BPCEg3IXAjuBVjXVNXDgQOksK1ascPrYQKPBuvjicimfipby10c8Io83yC0sk50eWyQ7PbZILln2p7fFscNkMsnu/1ksOz22SP6SebTRz+/We6WyXGk7T0VLOauTlEaj++quhR07drilnsLCQqeOy8/Pl506dbLbdvbsWTlgwAA5b968etfzxx9/yJ49e8rc3NxqcrVv375aeUd/N5Aha7Cpnhzj3wAkCiE6CyGCgWuBhbYFhBADgNnAZCnlSU8I4Y7Y1YFCg3Tx9zuw/09olwIT/d8vu4pWUaHcN0aZeDZ3r/MRGD3Bo/O3UmEwcV7XOLfm0q0vbr1XdCHwsDn/cdkZeDZGSdHpJzjrzumOsMybN2/mrrvuYuHChbRq1cpun8+HZZZSGoD7gKUoPfq5UsosIcSzQgjz2x/+B0QC84QQW4QQC2uozmn8JRBXY1BvXRQcht/Nbnk3/qAk3ggg/jk2Ea1GsDy7nMJy35ia//DcTOZtzCFEp+HTWwd5RQa33ytRreFRm3hJL3WCwuPuPYeHiIyMdPpYV8Myz5gxg+LiYq666ir69+/P5MmTLfvcFZbZY0M97vy4MtSzbt06p48NNOqlC5NJyneHKo/oh9I8L5SX+HNXruz02CL52PxMr8phMpnkC4t3yE6PLZKJ/1ksTxeVe00Wj90rJpOUX11lHfrRl3nmPNJ9Qz1FRUVOH7tx40Z544031llu/PjxDa57xIgRMj8/v9p2Xxrq8QmqXgSp1FMXGZ/CyR3Q/0bo6FqaOF9mTI9WtIkQfLfhCJVG7w35zF59gNmrDnBhr1ZkPjmeuEjvhQrw2L0iBNwwF/rfoKwv9n2XYOnCHAQ1LLOKf1GSB78+pPjrXxz4c+ku7aIkNfm0jvDNnuKvfad5eckuLuzVmtk3pRIWHFhDatW47F1o2x82fwl7fve2NB5FDcvsZfwpobanqVMX31ytfF/7tdd8rxuTB6coeYJf/G0Xhkbu9Z8treSB7zaT0CKCN6/tj7YR/fVrwuP3ihBw449Kx2LeLWD0jfcrjnAlEYs/EPCG/+BB7/TmfJFadbHlWziaAYP/Ad0ubDyhvMiRw4e4c4Qyaenb9Ycb9dzPL95BQWklb107gIgQn5hO0zj3SkScEvajshQ2feGRU7gyTFNF1XwMf8CZvzfgDX/V5A6VWnRRlAtL/w3BUTC+6eRNPXHiBNPNrp3/tyCr0cb6Nx0+w9yMHG4f3pk+7Zs1yjnrQ6PdK0Pugej2yrCivrTu8g0gNDSUvLw8l42/wWBwk0SeRUpJXl4eoaENC+TnG10NFe9hqIC5N0FFEfxjheJ/3YRoHh7M9UM68k36YR74bjPv3TDQo+czmSTPLMyiVVQI94/1nST1jYpWB5NehW+vhTWvwtiaQyA0lPj4eHJycuqMh1MX5eXlDTam3iI0NJT4+IaFOQt4w9+797kBQZsu1XQhJfzxFBxJV27ENn29I5iXqNLHU5f25pv0wyzedoJFW495dPLUF2nZZOac5eWp/Yj0kSGeKhr1XulxETTrCGteUYYXo1q7pdqgoCC3xBw6efJktclTgUTAD/XU5VLVlKimi7R3IP19SL4eBt3hHaG8SJU+QnRa5t6lvNi875vNXPbuX5wscj4ee02cLCzn1T/2kNophqt8MNxyo98rF5lnhH9xWe3lvECg242AN/xVgbVUztHFug/g95nQYShc+qb3hPIitvoY3DnWEr4580gBg59fTt+nl/Lh6v2UV7puBIwmyT1fb6JMb+TpyUk+meGp0e+VnpMgpjOc2qk8efoQgW43At7wqzhg4+ew5DFoPxBunA+6YG9L5BOMT2rDxpkXMr53a5qHB1FUbuCFxbtIfuZ3thwpcKnuN5btYeOhM/xnUi+feqHrde5Yrnz/9QbsXuJVUZoSvjXI6AHat2+cfKX+QPv27SHrZyWFYosecMN8CInytlhew1HbiIsM4cObUwEwGE18nnaI5xbt4PJ3/2LOtEGM7lH3uG+p3sCxgjJOnK3g2Nky5m44QsahM1wxoD23npfg7j/DbXjlXomIg3+shA9Hw7fXwLD7FM8yLz8RBbrdEO7wefU0qampsipueUMpKytTI3Saqdi2gJCfbodWPeHmhRAe622RvEp928a6A3lc++E6AL65YwjndXMc5nzjoTO8v3Iff+46iemc2+r+C7rxwNhEdFrffcj26r2y9w/42hy9slkHuHkBxNU/YYm7CQS7IYTYKKVMdbTPd1uhm6hKltHk2bOU4B9uhZY9VKNvpr5tY2iXOH6efj4A13+cTtr+PLv9ReWVPDw3kyvf/5uMQ2e4c2QXXryiL+/dkMI3dw4h86nxPDy+h08bffDyvZI4Dh7LVt45nT0Cb6d4NXVjoNuNgB/qUQEOrILvb6QkohORt/yiGn0n6N+hOe/dkMK9X2/iuo/Wkdophkcn9mTb0bPMXrWf08UV3DemG/eO6Up4sHpbOUVYDNy+FPL2w093wY93QkVhk/Q48zS+3QVxA67E1Q4Ijm2B72+CyNbsH/qiavRtaGjbuLhvW5Y8OAKAjENnuHp2Gs8t2kFsRDA/3HMej0zo4ddG32fulbiucOuv0PE8+PVh2PJNo4vgM7rwEAE/xt+kObIevrwCwprDtN+geQdvS+TXnDlRwukjxST0b8GPW45ysrCCPu2bMap7S7uk6KWFenasPUrPYe2IjGlaM6Hdir4Uvr4KctYr7Tfe4XC1Sg006TH+qmTNTY4DK+HLKRAaDbcshOYdmq4uaqAh+ig4Wco3T6fz+ydZfPKvNVwzqCP3j01kTM9WdkbfUGnks0fXkr7wIJ8/8Renc4pqqdW38Ln2ERwO13wJUW2UH4DCY412ap/ThZsJeMPvT1H23Mbmr+CrKyGyFdy2FGK7AE1UF7VQUVFBRWklC97YzNKPt9ca2OvrJ9dZlo0GEwvf2uKw/Kal9lE+v//vBk4dcWz8Tx0u4t27/+Tdu//kyM58J/8K9+GT7SM8FqZ8CPpi+O4GqHT/jGpH+KQu3EjAG/4mhckEvz0GC6ZDuwFw+x/q8E4tSCn5+KE15Ow6w76Mk8z+5yqHxvxgZvWAX0d25LP4/W1228qLK9mwqHpo47nPb8Cgt5/9azKamPvCBsv6wje3sO7n/c7+KYFNp2Ew9TM4tkkJ72DyXsa0QCHgDf/w4cO9LULjkLcfPp0A6R9Ayi3Ky7EIe3/zJqOLeiCl5OwW+xm0xkoT792zAoNNiIYDm09VM/BVZG89benNm4wmPnlkTY3nW3/OD0LWmurDFhuXHKIg13GYYkOlkRMHzlKp91wMGZ9uH70ugYG3wpF18OXlUF7o0dP5tC7cQMAb/j179nhbBM8iJWz+Gj4YAblZStydS990GF454HVhw7oF+3n37j9ZMnsbpnNnUwGL3snk6O4Ch8fOvn8VJqOJwtNl/Dbb3uh3Trb/MZ37/AZMRhPvT19pt/2S+5Lt1jf/fhijOd5/eUklq79zfC2+fmpdtW2FeWXMvn8VP7y8kQ//uYofXvaMo4PPt49L3oCkKXBwFbzSHdLeA32JR07l87pwkYA3/CdPnvS2CJ6jJA++ux4W3KuEVL73b6VXVMN094DWhQ0/vrKRjb8dAmD/5lN8+Z+/kTbG/8iufA5n1T6m/v70lXw50/4F36X3J3PxPf3oMbRNtbK2dE5uQac+cdz19ii77Su+3EXuwUI+edj+ySAs2j5Wku0PlZSSL/9jL8eJA4VkrTlaq/zO4PPtQwi4ag5c+y1EtISlT8CrvWDZM1DsWvz9c/F5XbhIwBv+gGXHQngnVZnqPvZJZWgnJsHbUnkVk0ny7t1/cnzfWbvtxWcqeO/eFWz6/RD6cgML39hit//Wl87n6n8PqrXu7oNb0zEpDoALb+1N0oiaY/aPvyMJAF2QljvfGGnZvnvdCea/ZN9bv/P1kdz28nC7J4QlNk8Z2VtPOzzHyq93Y6yseaxbX26wG7IKKHpeDA9uhWu+go5DYO3r8NYAWP+ROv5fTwLe8Pfp08fbIriXs0fh2+uVrFnR7eHOP2HEw0pWozpoTF1IvR79kSMYi4vdWu+2lTks/2InxWeqe1388taWWo9N+3E/Hz242m7b1f8eRESzEFp2jKrR+LfqFMW425Lsto26vofDstc9NQRdkNayHhyqI2VCR4dl+4xqT3CYct069YkjvJnS8z+YeZrd6SfI3na6xvcLAFuWV88TnLP7DO/e/ScfPbia2fevYnd6/dMp+tW9IgT0uhRumAf3pkHbfrD4ESXQW6nrHlJ+pQsnCHjDHzBuWYYKZUzzvaGw7w8Y/W/F6LftV+8qXNGF1OvZf8kl7OzZiwOTL8NYXPPYasm6dHb1S2b/uPHsSR3E0Ucfrbleg4Fjjz3Ozp692NmzV40/FPs3n+Tdu/9k9Xd72PX3cT5/4i+yt1l7w6cOF5Gz64zdMXe8PvLcauwYfUcnWna0Ridt2TGq2jEajWDq49XnwAghuP2VEXbbbnxuGLFtI6qVHXq542BjI6/pbrc++Z/9LcvLPtvBr+9utawPvKgT0z+4gOkfXGDZtu7nA3ZeSMf3n2XB65vt6lz22Q4qSisdnv9c/PZeadULblkEE1+Cfcvgowvg+Na6j6sFv9VFPQn4mbsrV65k9OjR7hWoMTHoIfNbJTdpwSHoegFc9DK0aHi+Vke6OPnGG+R9MBuAHhsz0ERUN1wA+y+ehP7AAbttiWtWo2vZ0m6bqaSE3QOrG8oOH31I5Ah7Q2kqLWV3SvUct7127bRb37LsMH/N3+f4j3JA1wEtmXBnH4RGIE2SRe9u5XCWfWC1i+7qy+GzWQ7bRlmxnm+fSScyJpQrZwxEG1Rz/6j4TDmHtufRfUgbgoK1NZY7siOfheYnkmFTujJgfEeHyVjev3eFw5fRd787Gq05yFt5SaXlPUFiaivG39EHo8HEB/etdHjuuPhIrnqgF8UrVxHaqych3bpZ9hkrTRScLKVZqzDW/rWG83v14ui/HqJ81y6ixo4lYtgwml8xxWG9UkpkeTnSaMJUeBZd69YIbc06aBQOpSnvvSrL4MKnlbSOmob3b/3eblD7zF3V8Psqpfmw6XNInw1Fx6FtMlzwf9DtwjpjlZvKyij64w+0MbFEjrC6pZ2ri5OvvkreRx/bHdsjcwuaEHuPoIq9ezlw6WSH5+q5c4edAds/YSL6Q4ccl92+DaGzDkntGT4C4+nTSATFke3RGvWEl52k7Ysv0nzK5QAc2HKK3z6oebjjXNp0iebKR6u3dUOlkZ9f20zuwUJ6DmvD2Ft6+2TbKC3U89mja+22TX08ldYJ0XbbPnl4DeUljnvy4c2CuezBAWStOcrWP3MAOC/tP4RWFFjKRJx/PiU3z+TPb60/5kmJu2n90VsO6+y67A+CzQm9pZTkfzaHky+/XK1cp6+/Inyg44T1Ukr02dnoYmLQNm/usIxbKDwO82+Dw38r982w+5UcvyH1j7/ji22joTRpw79//366dvVeXO8Gc3wrbPhYCUlrKIOEETD8Qeg6tl7JKUr+/pvDt91uWY+84AI6vPcuYK+Lsm3byL7qaod19Mzabum5ycpKdvW1Did1/X0p+8dPsKw3v+oq2j73rLnO7WRfdRUAbZ//L82vvJKyzEyyr7kWgOiLL6L9a68BULpxI5n3PMmmlIernb/v9tm0eORRDp8MYf8mq3dFZHEO/TPfJqiymE0DHuJss+rXtapnbDx7lqOPzEBWVNDmmacJcZCA21fbRkVpJTv+Ok5lhZE+I9sTHl09Q1r+kQK+fX5Tte1aneDud8YAYCwp4YOHreGFR6+6H41UXn5u6Ted/NjqydXHrLwPges2QdemDW2efBJ9drbDHwhQngLDBw+u1tFwC1IqHaeVs5SOkzYEOg6FrmOgx8VKePJa8NW20RCatOEvLi72/Uh7pfmQ9RNs/hKObQZdGPSdCkPuhjZ9qDxxguxrrsWQm0tQx450/ORjgjtUn5FbtnUr2VdfU217i3vvpeU/77fowpCfz97zzrfs756RwcmXXqJg3jwAgjp1pNvSpUiTib3DR2DMV16WJa5dg66F4sd+6OZbKF2/HoBO33yNsaCAnHunW+q0Ha7J+2wOJ196CYDQvn2Ju20aW/47h21976q3ilI3vkR0kfIys8NHH3HkzjupCG7GyVYpyCm3EZsQR8rETmi1GvRHjrB/3Hi746MvvZR2L79k93TiTNuoul+M0ojeqCdIG4RWaDGajOg0Opdz6crKSgoXL6bwtyU0mzKFqPHjqtVZtHIlOXffw9G257O7x/V2+0aueYjYCRcQ0qMnp15/nZLw1qQPftJcuYnRqx9gb7erONre+i5DZyjFoAsHoHnBXoaVLyFyzGhK/vqb8q3KWLlBG0pmv3spC2tJn+0f0bzQftjPFURYGK3+9S9KN6yn8kQuhrzTGI4dt+xvNWMGUePHOWzzdWI0KHGr9v0BB1fDyR3K9nYDlGGgPlMdph71C7tRB03a8PvqI5vMP0L+W89TumkjrbplExKth1ZJkHITJF+rxCYHKo8fZ9+YC6od33nBz4T2sPZazh0vb/PsMxT88APlmcqN2/bFF9kS05xRo0axq5e1p5eY9je6GOVcx//vSYvxD+ndi4odVuPd9r/P0XzqVKv8RiO7khx7PsT+sJysjWc5daQIjUbQNrE5Uc9cT1BlCYXRCWxLugN9SHO7Y+LaR5B31PEL477bPqBl3jZCevak848/IDQaKnNPsm+U1U++x6aNiLAw8mZ/yKk33nBYj6lzPLmzn+Dg2YOc1Z9l+8HtRMRG0HrbcSZ8sYvIEiNZnTS8cZngbIQbU/9JSbAB9DpACGJDY2kRpvyAtg5vTbA2mObBzRj9bjqtN1UfJmv/9ltEjR6NCAoi98VZ5H/+uWVfWUgsWcl3Y5SC/pnvEKI/W+34Xd2v5Vi7EdW2A4xa/SBaUyX5MT3YkvxPAC6c1pseQ6xzFbLemc/K7fbhvHvu/poRXz5jGf4BKN2wgUM33Vx/vThBzPXXE9ytK7rmzQnq0IHyXbsoy9hIaFISkSNHoI2JQduslpzGhceU9KObPodTuxTPuCF3wYCb7EKW+6rdaAiq4W+kCyiNRoqWLafy+DGiL7qYoNY2+VkLj8GRdMj+C8OO1ez9xD5wlwgOont6OhqbdG8mvZ7d/WxmgOp0YDBYVrstX0aQOTfo7kGDMRUpdfbcmokIVnoxB6ZcQcVOxYAbY2LQFhQoj8FA12XLCI635haVUpJz73SKV6ywky1y7Fg6vPtOtb/3XONbFhJL2rDn6tSTLf94cxRBIdYXgmdPlZL21RYK16YRU7CX1rnrCTKU0eyyybQzPzVUGisp1BdybF8muqum11Q19z0YzqmQCm7/3cT4zcrfnJkgeP465Xwak+SlT410cjD3571JGlb2s38pGKYLo01oKwauOsHVvys/Ulk9wvj4qmhy5Vm0QotJmgjRhhCmC+NU6Umm/WHioo3299hXYzQsHCIsQ3ehFZIvXmu4z/3WJy7HNKQfzUOa09YURfQXv1Hx/Y92Zdo89yzNp05l/ksbOZltH+bgzid7kffRh4R07UbzqVcy94O/ydul7Js0vR+d+sSxfM7OGl1C+1/YgfOnVncyOLg5l8Wzs9BgZFizLPo+cWu1MX19djZnvvue/DlzGvx31wdtyxY0m3QJYQMGED4oFU1ICJXHj6ONi0MbFQVGI+LwavjrTcheA5og6DIKOo+Etsms232CoROudurFsK/gNcMvhJgIvAlogY+llLPO2R8CfAEMBPKAa6SU2efW44rh37RpEykpKU4dC4prYuGS35D6SppfMYXwVMcxwfO//prc5/5rty28Zzs6XtsBcWw9FCozLcsKo8heXHOC826rVhLUunU1o1/1EvXcnnZwQgL67GzLevcN65WGbUZKyf6xF1J5zD42TKcvvyB8UHW/dSklBydfRsXevQA0m3olLZ98mqzVx0j7aT8mo9JeNFpBfM8YWrXScmhlFqdk3UnIbemRGsvY25MRQlBuKOdk6UlOlp6k1FBKfnk+Bft3MeQBpWebPr4Dv1wYxZnyM+SX52OUViPZskDy7vv2RvNQS3jxHzGEhkURFRxFiC6E1MPBTHyjejgEW8IHDaJ0gzVwWkivXiR8/x2a4GCkwcDRGTMo+m2Jw2PDkpPp8PFHFt0XrVhBzj331no+ERaGLCuz27bv8gGsvzCeEp2BsNyz3PjU39WO29ZJ8N/rNEhHw0pS0jM/jI7lEZzo14648BZEB0cTGxpLxM4OGP6KIzZZQ88pzWgW1ow24W0I04UhhGDTpk3sW2Lk1OHq0UTj4iO5+t+D0GgE6QsPkLE4G4Chl3dh4MQEACorjPz06qbqxwu4ddb5RDSrPpavLzOwN+0ImpKzdDq/G+Gx1YdXDHl5lPz1F8cefawGTbqGCNIR3jmGZh0KCAk6ijQJNFqJNjIc2aoXmrZ90HQdiGg3AFr29JsfA68YfiGEFtgDjANygA3AdVLKHTZl7gX6SSnvFkJcC0yRUlYbpHZXIhZjQQEFP/1M0e+/oz9yBG2zZkQMHUqzKVMI62M/Qads2zZOvvQypeecNzSpF23//S9C2zWDwmPIghxOfLiAgr+s7oa6UCOGcmsvtv313YkeP5EzW0s48donAESOGkX8B+8jhMBYUMC+CRMxna3+mA7KGLw20upmKaVkT+ogTCX2QyOdvv2G8AEDHNaR/8WX5L7wAgBtXnwR3aiJFJwo5UxuKUX55ejLDGg0gpAIHRHNQig6doYzpyvJ3lFQh1ar0/Vu0EcVU1ZZRlFlEcUVxVSchrKTJk7E7KdQc4YifREFFQUU6YvsDPm5BGmCiAmNITY0lpiQGKJDomkR1oKIoAgigyJpFd6KNuWhRDz4IloTdHj3PUJ7dHdY17kvvqtIWLqUkA7xaMw39NlFv3LskUfsykigMigIKQTBej3xb7xOUJs2HL79jmrXwajRUBkURFBlJRqTic4/zCcsKQlpMFD4668ce+xxS51SCISUCOyH3WwxlZRQsm4dlcdPED54EKHdu1NuKOfA2QPkluRSYaogvyyfUkMpuSW5HCs5RpG+iCJ9Efnl+eSX1z2hKTo4mpjQGCKDIhmQdjmRedYfct34XOL6BBERFEG4LpyIoAgMOcFs/FiZRxEUqkWjE1QUW59GR1zTHUOlkbQflYijGo1gyowU2nRWhmHKivVkLjvCxqWHsH2X3DEpjvOu6Epce/sfgIoyAzk788k/XkJlhZGo2BDiu0URE9/cXlelpRQuXszxmf9HpS4MkyaYoMoiy0ttdyC0gqC4CHRxzRDBYWjCw9FERqGJbEZQp85oW7VDhISgi4lB16YtmrBQtNHRiPBwl98BNUhOLxn+YcDTUsoJ5vUnAKSUL9qUWWoukyaE0AEngJbyHKGcNfw/vvYPjEYjWo0AvRFRFdlQI5BaDUiJMPdgpU6DDNIAElEpwWhCACadQOoAk0RjAEzmX3uNRGpAmASYAA0YwjSg1YLQgtCgKdWjqTRUk8sYEYoM1lnau0CAlGjK9Ai9wbwNTBoNxshQ8xrmG0RaVoXBhKZcj9RpMQUHKSM4UlhvJGk+VCgr0mQ+l8na+KS1diTSfKi02y4QSI0Rk86ACaOyxwTCpEWDFhMm9NpyDBo9QmjQSR1a89Gyql5hQggQQoP9Px0ahCKXEGg0AqERCCEshthoMmEwmaoqw4QJgbmsUCZTSSQGkwm9yYAEjNKEXhowSBMRIhiNEGiFBg0Co76CSowYNAK9qN7+g4SGcHQYK8rRC9DX4scfJDQEo6HSUIleW/NNHSQ0hAodJikxITGaTOhFdWMUKnSEaoLQCsW3xiQlBoyUGiuRSDRCgwDCNMGEaYPQCS0aoVxzAyYqTUaM0oSUEiEEOqElWKNDi3KclCaMmDBIIwZpwCRNmJAgUV5Qm+vHBBgFem0FJhSZpVnvEtAg0JqCCK+0cTOVgkptORUhpUpZoUEDBFeGEaxXXh6bNEYQJoRBceuVWiOGoEqEAI1Bh6YySGkxQUZkkHK/CoMWKrWW629BAFqJCJYIrbnFGgGDwGQw35dVaCUaHUqWcY1U7hMTUGkEI8q9YTIBUvkhliaE+cdCCtsTVrVr81CdlGaNmKrLVifWOwyN7eHC5qbUcuXDr6MJrv4Cus7aazH8nkwQ2h44YrOeAwypqYyU0iCEOAvEAXYBSo4fP04P84vMkJAQpk2bxgBzzzYuLo6kpCRWr1am4ut0OoYPH86mTZuI7LsKrba64VVRUVHxByr1Yaz+6y+6dO1KVFQUmZmZQM12b+bMmcwzO2gALRzX6tke/1RgopTyDvP6TcAQKeV9NmW2m8vkmNf3m8vYGX5ne/zLP36eM4WFNG8ZB0FBCI0WcxcRhEAIjfLoJQRCo4NKA2h1CJ3W/IursfzIK1/C9kff4lYvzBuq6jJvNH8pdQhhc7y5rBD2Pcmq/VVVaMw9X6EVaDUaez9+Kc1lq2Sq6jeb60Cg0Si9QQ1KPbt376FHz542soFQum9K3UapdFyUriZCI0CL8i00aEI0dtLKSgkmiTSa0Ght9BqsMR9j7h1JiUlvwKg3Wnv2QRp0oUFotVqzLgQaIag0GDDpjWikQBpBINGFBCFCtAiNxvr3AlUdLZPeiJACEaRBo9UgNBrQCvPlU54GMAEGE9Kg/IFSwpbMTAYM6K/IWiWvRijlKo1IoyK7qLrYGuUxHyEQOuXpEKNEmiTSrLtzLqjj5RrKiBq2VztYgDRJjEYDJsv9K5Wev9AqbUWjPGmZDAaM0qj08g0GxR1VQrAuCCEFmKxPkJlbttCrXxJ6QyUmoxGTyYRWaBBSoDVHd5GWZzhz4zHXJ81PDZgfqk1GqczsRXn6MGJSIqSazOXNvXehUf4W+7at/KevMCAAXYjOqgET5gdOpV6TNGEymagoNwAmtMEaJOanGAczoCVgMBjQaDSWlJnSpq9dtXzs6DHatW9b/WBpI6KzSEAaFCWYjFhuOqXB2VQu0Qgdwyf/06nTeKvHfxSwdbyNN29zVCbHPNTTDOUlr1sYe8d/MJlMliGDpk6nXqNVXdgwolVvVR82jO2QqurDTKDbDU/+ZRuARCFEZyFEMHAtsPCcMguBW8zLU4E/zx3fd5WsrCx3VufXqLqwR9WHPao+rAS6LjzW4zeP2d8HLEVx5/xUSpklhHgWyJBSLgQ+Ab4UQuwD8lF+HNxKXp7bHiD8HlUX9qj6sEfVh5VA14Unh3qQUi4GFp+z7Umb5XLgKk/KoKKioqJiT+AOYplJTk6uu1ATQdWFPao+7FH1YSXQdRHwhn/27NneFsFnUHVhj6oPe1R9WAl0XQS84f/ss8+8LYLPoOrCHlUf9qj6sBLough4w6+ioqKiYo9fROcUQpwCqserrR8tOGcmcBNG1YU9qj7sUfVhJRB00UlK2dLRDr8w/CoqKioq7kMd6lFRUVFpYqiGX0VFRaWJEbCGXwgxUQixWwixTwjxuLflaWyEEJ8KIU6aA+FVbYsVQvwhhNhr/q4e/D0AEUJ0EEKsEELsEEJkCSEeMG9vqvoIFUKsF0JkmvXxjHl7ZyFEuvme+d4caqVJIITQCiE2CyEWmdcDWhcBafjNSWDeBS4CegPXCSF6135UwDEHmHjOtseB5VLKRGC5eb0pYAAellL2BoYC083toanqowK4QEqZDPQHJgohhgIvAa9LKbsBZ4DqWWsClweAnTbrAa2LgDT8wGBgn5TygJRSD3wHXOZlmRoVKeVqlPhHtlwGfG5e/hy4vDFl8hZSyuNSyk3m5SKUG7w9TVcfUkpZbF4NMn8kcAEw37y9yehDCBEPTAI+Nq8LAlwXgWr4HSWBaV9D2aZEaynlcfPyCaC1N4XxBkKIBGAAkE4T1od5aGMLcBL4A9gPFEgpqzIXNaV75g3gUawZFeIIcF0EquFXqQNz+Osm5csrhIgEfgAelFIW2u5ravqQUhqllP1R8mQMBnp6VyLvIIS4BDgppdzobVkaE49G5/Qi9UkC0xTJFUK0lVIeF0K0RentNQmEEEEoRv9rKeWP5s1NVh9VSCkLhBArgGFAcyGEztzTbSr3zPnAZCHExUAoEA28SYDrIlB7/PVJAtMUsU18cwuwwIuyNBrmMdtPgJ1SytdsdjVVfbQUQjQ3L4cB41Dee6xASYgETUQfUsonpJTxUsoEFDvxp5TyBgJcFwE7c9f8C/4G1iQwz3tXosZFCPEtMBpl6nku8BTwMzAX6IgSAuNqKeW5L4ADDiHEcGANsA3rOO6/Ucb5m6I++qG8sNSidP7mSimfFUJ0QXGEiAU2AzdKKSu8J2njIoQYDTwipbwk0HURsIZfRUVFRcUxgTrUo6KioqJSA6rhV1FRUWliqIZfRUVFpYmhGn4VFRWVJoZq+FVUVFSaGKrhV1FRUWliqIZfJaARQsQJIbaYPyeEEEfNy8VCiPc8cL45QoiDQoi7nTx+hVm2VHfLpqJSRaCGbFBRAUBKmYcSehghxNNAsZTyFQ+fdoaUcn7dxaojpRwjhFjpZnlUVOxQe/wqTRIhxGibpBtPCyE+F0KsEUIcEkJcIYR4WQixTQixxBznByHEQCHEKiHERiHEUnN8n7rOM0cI8ZYQ4m8hxAEhxFTz9rZCiNXmp4/tQogRnv2LVVSsqIZfRUWhK0oM9snAV8AKKWVfoAyYZDb+bwNTpZQDgU+B+oYBaQsMBy4BZpm3XQ8sNUfITAa2uOfPUFGpG3WoR0VF4TcpZaUQYhtKDJsl5u3bgASgB9AH+EOJ+YYWOO6gHkf8LKU0ATuEEFUx/zcAn5p/UH6WUm5xy1+holIP1B6/iopCBYDZQFdKaxArE0oHSQBZUsr+5k9fKeX4htRtRpjPsxoYiRLud44Q4mZ3/BEqKvVBNfwqKvVjN9BSCDEMlPj+QogkZysTQnQCcqWUH6Gk/Etxj5gqKnWjDvWoqNQDKaXe/GL2LSFEM5R75w0gy8kqRwMzhBCVQDGg9vhVGg01LLOKihsRQswBFjnrzmmuYyVKXPgMd8mlomKLOtSjouJezgLPuTKBC+gCVLpVKhUVG9Qev4qKikoTQ+3xq6ioqDQxVMOvoqKi0sRQDb+KiopKE0M1/CoqKipNDNXwq6ioqDQx/h/2fWcMoiPbhQAAAABJRU5ErkJggg==", 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -953,19 +975,25 @@ }, { "cell_type": "markdown", - "source": [ - "Now we plot the dynamics for the control in the excited state." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "Now we plot the dynamics for the control in the excited state." + ] }, { "cell_type": "code", "execution_count": 23, + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + }, "outputs": [ { "name": "stdout", @@ -985,8 +1013,10 @@ }, { "data": { - "text/plain": "
", - "image/png": 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\n" + "image/png": 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", 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\n" 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", + "text/plain": [ + "
" + ] }, "metadata": { "needs_background": "light" @@ -1012,37 +1044,31 @@ "\n", "plot_dynamics(exp, init_state, sequence)\n", "plotSplittedPopulation(exp, init_state, sequence)" - ], - "metadata": { - "collapsed": false, - "pycharm": { - "name": "#%%\n" - } - } + ] }, { "cell_type": "markdown", - "source": [ - "As intended, the dynamics of the target is dependent on the control qubit performing a flip if the control is excited and an identity otherwise." - ], "metadata": { "collapsed": false, "pycharm": { "name": "#%% md\n" } - } + }, + "source": [ + "As intended, the dynamics of the target is dependent on the control qubit performing a flip if the control is excited and an identity otherwise." + ] }, { "cell_type": "code", "execution_count": null, - "outputs": [], - "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } - } + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -1069,4 +1095,4 @@ }, "nbformat": 4, "nbformat_minor": 4 -} \ No newline at end of file +} From 286e7bfe2cf8c20265210eb116450d05fdad1cae Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Thu, 23 Dec 2021 16:56:59 +0100 Subject: [PATCH 77/80] add restructuring of experiment and model class --- CHANGELOG.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 09615f8f..8ff2ff18 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ This Changelog tracks all past changes to this project as well as details about ### Details +- `changed` the experiment and model classes for handling different propagation methods #144 - `changed` The generator now can handle any list of devices that forms a directed graph #129 - `removed` official support for Python 3.6 #156 - `added` a method to HJSON dump current parameter values #149 @@ -130,4 +131,4 @@ This is the first major and properly packaged release of the `c3-toolset` librar - `added`, `fixed` Compatibility with Windows and MacOS - `added` Better Examples and Docs - `added` pip installation configs -- `added` pip-test deployment github actions \ No newline at end of file +- `added` pip-test deployment github actions From f9a9377498140157ea5a74b8668596a72b6a6186 Mon Sep 17 00:00:00 2001 From: Nicolas Wittler <38694261+nwittler@users.noreply.github.com> Date: Thu, 23 Dec 2021 17:49:56 +0100 Subject: [PATCH 78/80] Update CHANGELOG.md --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8ff2ff18..39bea0a9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -13,6 +13,7 @@ This Changelog tracks all past changes to this project as well as details about ### Details +- `added` experimental support for Runge Kutta 4 solving of EOM #144 - `changed` the experiment and model classes for handling different propagation methods #144 - `changed` The generator now can handle any list of devices that forms a directed graph #129 - `removed` official support for Python 3.6 #156 From 654d6c765bf4c24782b85b852ef719e6845d2383 Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Thu, 23 Dec 2021 17:47:26 +0100 Subject: [PATCH 79/80] bump version to 1.4 --- c3/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/c3/__init__.py b/c3/__init__.py index 9f6cc160..e9923993 100755 --- a/c3/__init__.py +++ b/c3/__init__.py @@ -1 +1 @@ -__version__ = "1.3" +__version__ = "1.4" diff --git a/setup.py b/setup.py index 8cf1503c..1e8151b7 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ setup( name="c3-toolset", - version="1.3", + version="1.4", description="Toolset for control, calibration and characterization of physical systems", url="http://www.q-optimize.org", author="q-optimize", From 435ec20e68144e314e17d9959a728019f7a6089e Mon Sep 17 00:00:00 2001 From: Anurag Saha Roy Date: Thu, 23 Dec 2021 17:51:49 +0100 Subject: [PATCH 80/80] set version 1.4 in CHANGELOG --- CHANGELOG.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index 39bea0a9..1499cf87 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -9,7 +9,11 @@ This Changelog tracks all past changes to this project as well as details about - `fixed` for any bug fixes. - `security` in case of vulnerabilities. -## Upcoming Version +## Version `1.4` - 23 Dec 2021 + +### Summary + +Maintenance updates to code quality and implementation along with some useful utility functions and example notebooks. ### Details