ro_optimization in autocalibration #392

Edoardo-Pedicillo · 2023-06-08T15:15:11Z

This PR implements the ro frequency optimization and a new cumulative function using numba.
Checklist:

Reviewers confirm new code works as expected.
Tests are passing.
Coverage does not decrease.
Documentation is updated.

Jacfomg · 2023-07-05T13:26:26Z

Can we try to have this working, or if you have to work on something else can I take it ?

andrea-pasquale · 2023-07-06T09:05:32Z

We might need this also to fix #337 given that when optimizing the freq we can clearly see in which direction the clouds are rotating.

This reverts commit f04b0eb.

…to auto_ro_optimization

codecov · 2023-07-08T17:08:39Z

Codecov Report

Merging #392 (48e2476) into main (4c54480) will increase coverage by 0.11%.
The diff coverage is 100.00%.

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #392      +/-   ##
==========================================
+ Coverage   97.03%   97.15%   +0.11%     
==========================================
  Files          48       49       +1     
  Lines        3107     3233     +126     
==========================================
+ Hits         3015     3141     +126     
  Misses         92       92

Flag	Coverage Δ
unittests	`97.15% <100.00%> (+0.11%)`	⬆️

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files	Coverage Δ
src/qibocal/protocols/characterization/__init__.py	`100.00% <100.00%> (ø)`
...bocal/protocols/characterization/classification.py	`100.00% <100.00%> (ø)`
...zation/readout_optimization/resonator_frequency.py	`100.00% <100.00%> (ø)`
src/qibocal/protocols/characterization/utils.py	`94.82% <100.00%> (+0.54%)`	⬆️

Edoardo-Pedicillo · 2023-07-09T07:29:50Z

Sorry for the number of commits, but I had some troubles with the stash.
Now the PR is ready, I tested it on hardware only with one qubit, when I tried with multiple ones I lost the connection with the instruments and I am not able to reconnect to iqm5q for the time being. These days I have to attend a school, @andrea-pasquale @Jacfomg could you please test the routine with multiple qubit?

andrea-pasquale

Thanks @Edoardo-Pedicillo.
I still need to test the routine on hardware, generally looks good to me.
Please find below some suggestions.

src/qibocal/protocols/characterization/utils.py

src/qibocal/protocols/characterization/readout_optimization/ro_frequency.py

Jacfomg · 2023-07-10T06:49:29Z

Sorry for the number of commits, but I had some troubles with the stash. Now the PR is ready, I tested it on hardware only with one qubit, when I tried with multiple ones I lost the connection with the instruments and I am not able to reconnect to iqm5q for the time being. These days I have to attend a school, @andrea-pasquale @Jacfomg could you please test the routine with multiple qubit?

I was testing it and the cumulative takes the times we measured. Now all the time the routine takes is on the execution. As there usually short time-scale noise going on that can be seen on weird blobs that decrease fidelity I suppose we should set a high default value for nshots if we want to avoid to do software averages. Also, checking if just using a high number of shots would help could be measured running a calibrate qubit states with many points using now the new cumulative function.

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

Edoardo-Pedicillo · 2023-07-10T17:07:23Z

I suppose we should set a high default value for nshots if we want to avoid to do software averages.

Yes indeed, I have not implemented the software average for this reason. Maybe I can add this as a suggestion somewhere.

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

andrea-pasquale

Thanks @Edoardo-Pedicillo.
I tested it on hardware and it seems to work as expected.
Just a few minor comments than we can merge.
Could you also have a look at the coverage?

src/qibocal/protocols/characterization/readout_optimization/resonator_frequency.py

andrea-pasquale · 2023-07-11T06:51:38Z

src/qibocal/protocols/characterization/readout_optimization/resonator_frequency.py

+            iq_state0 = data[qubit, 0][data[qubit, 0].freq == freq][["i", "q"]]
+            iq_state1 = data[qubit, 1][data[qubit, 1].freq == freq][["i", "q"]]
+            iq_state0 = iq_state0.i + 1.0j * iq_state0.q
+            iq_state1 = iq_state1.i + 1.0j * iq_state1.q
+
+            iq_state1 = np.array(iq_state1)
+            iq_state0 = np.array(iq_state0)
+            nshots = len(iq_state0)
+
+            iq_mean_state1 = np.mean(iq_state1)
+            iq_mean_state0 = np.mean(iq_state0)
+
+            vector01 = iq_mean_state1 - iq_mean_state0
+            rotation_angle = np.angle(vector01)
+
+            iq_state1_rotated = iq_state1 * np.exp(-1j * rotation_angle)
+            iq_state0_rotated = iq_state0 * np.exp(-1j * rotation_angle)
+
+            real_values_state1 = iq_state1_rotated.real
+            real_values_state0 = iq_state0_rotated.real
+
+            real_values_combined = np.concatenate(
+                (real_values_state1, real_values_state0)
+            )
+
+            cum_distribution_state1 = cumulative(
+                real_values_combined, real_values_state1
+            )
+            cum_distribution_state0 = cumulative(
+                real_values_combined, real_values_state0
+            )
+
+            cum_distribution_diff = np.abs(
+                np.array(cum_distribution_state1) - np.array(cum_distribution_state0)
+            )
+            argmax = np.argmax(cum_distribution_diff)
+            errors_state1 = nshots - cum_distribution_state1[argmax]
+            errors_state0 = cum_distribution_state0[argmax]
+            fidelities.append((errors_state1 + errors_state0) / nshots / 2)


This is the same operation that we are doing for the single shot classification, right?
We should be able to recycle that function in one way or another.
Did you speed up also the fitting in the single shot classification?

This is the same operation that we are doing for the single shot classification, right?

Yes

We should be able to recycle that function in one way or another.

If not strictly necessary now, I prefer to do it in the (near) future PR to port the classification models.

Did you speed up also the fitting in the single shot classification?

Yes, only the cumulative part.

src/qibocal/protocols/characterization/readout_optimization/resonator_frequency.py

…sonator_frequency.py Co-authored-by: Andrea Pasquale <[email protected]>

andrea-pasquale

Thanks @Edoardo-Pedicillo, looks good to me.

add ro_frequency in autocalibration

8b8ee51

andrea-pasquale added the deploy on hardware label Jun 9, 2023

andrea-pasquale mentioned this pull request Jun 9, 2023

fixed update qubit frequency after Ramsey #341

Closed

4 tasks

Jacfomg mentioned this pull request Jun 19, 2023

Possible new routines. qiboteam/qibolab#496

Closed

Merge branch 'main' into auto_ro_optimization

6248a31

Edoardo-Pedicillo and others added 21 commits July 7, 2023 12:00

deploy ro freq optimization

57ebe78

add numba dep

ca1379f

applied stash that had conflicts

31b6f03

applied stash that had conflicts

67e35f0

applied stash that had conflicts

e9ab173

fix cumulative bug and clean code

973130f

Delete actions_qm.yml

11fb556

Delete utils.py

c10f33e

Delete calibrate_qubit_states.py

f6ac7fb

Delete fast_reset.py

72d3ba7

Delete ramsey_unrolling.py

9e99a24

Delete ro_frequency.py

f04b0eb

Revert "Delete ro_frequency.py"

c3b63ac

This reverts commit f04b0eb.

remove plots in classification

0a3712d

update docs

a4c3ba5

add routine in tests

d5b2c7d

fix lint bug

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Merge branch 'main' into auto_ro_optimization

b11468f

fix tests

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remove print

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Merge branch 'auto_ro_optimization' of github.com:qiboteam/qibocal in…

f8d51f6

…to auto_ro_optimization

andrea-pasquale reviewed Jul 10, 2023

View reviewed changes

Edoardo-Pedicillo and others added 2 commits July 10, 2023 20:57

Update src/qibocal/protocols/characterization/readout_optimization/ro…

d7660e0

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

Update src/qibocal/protocols/characterization/readout_optimization/ro…

b78830b

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

Edoardo-Pedicillo and others added 8 commits July 10, 2023 21:07

Update src/qibocal/protocols/characterization/readout_optimization/ro…

edba7cb

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

Update src/qibocal/protocols/characterization/readout_optimization/ro…

e8a3b41

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

Update src/qibocal/protocols/characterization/readout_optimization/ro…

42a3dcc

…_frequency.py Co-authored-by: Juan Cereijo <[email protected]>

from RoFrequency in ResonatorFrequency

948b9c0

change routine name

af55eb4

remove sorting

dfff092

fix test

995b313

restore i, q

ed4161c

Jacfomg approved these changes Jul 11, 2023

View reviewed changes

andrea-pasquale reviewed Jul 11, 2023

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Edoardo-Pedicillo and others added 8 commits July 17, 2023 17:15

Update src/qibocal/protocols/characterization/readout_optimization/re…

3ee8a16

…sonator_frequency.py Co-authored-by: Andrea Pasquale <[email protected]>

update docstrings

fdfeb7e

unique_freqs function

a2d3cd1

add cumulative test

8251dd4

Merge branch 'main' into auto_ro_optimization

c3c43aa

change test options

dad3bc6

fix bug

4cb29bb

cast float

48e2476

Edoardo-Pedicillo requested a review from andrea-pasquale July 20, 2023 07:01

andrea-pasquale removed the deploy on hardware label Jul 20, 2023

andrea-pasquale approved these changes Jul 20, 2023

View reviewed changes

andrea-pasquale added this pull request to the merge queue Jul 20, 2023

Merged via the queue into main with commit ec7e5cc Jul 20, 2023

andrea-pasquale deleted the auto_ro_optimization branch July 20, 2023 07:09

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ro_optimization in autocalibration #392

ro_optimization in autocalibration #392

Edoardo-Pedicillo commented Jun 8, 2023 •

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ro_optimization in autocalibration #392

ro_optimization in autocalibration #392

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Edoardo-Pedicillo commented Jun 8, 2023 • edited Loading

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