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GreNADE

GreNADE is for quantum state reconstruction using Neural Autoregressive Distribution Estimators (NADEs).

Usage

Firstly, include NADE.jl.

include("NADE.jl")

NADE.jl includes all relevant functions to train a NADE. However, the user must specify the following.

  • train_data : a file containing samples of binary data
  • Nh : the number of hidden units

Now, to initialize the NADE parameters, call the initialize_parameters() function. There are two keyword arguments for this:

  • seed: (default: 1234) the random seed for initializing the NADE weights
  • zero_weights: (Bool, default: false) choice of initializing the NADE weights to zero or not. Of course, this will override the seed if it was specified. So, initialize_parameters(seed=9999, zero_weights=true) won't do anything with seed and one could have equivalently called initialize_parameters(zero_weights=true).

The biases of the NADE are always set to initialize to zero. Now, specify what is needed to call the train function to train the NADE.

  • train_data: binary input data
  • batch_size: (integer, default: 100) the mini batch size used for calculating gradients
  • opt: the optimization method (e.g. ADAM()). These are optimizers available in Flux.
  • epochs: (integer, default:1000) number of training steps (passes through the input data)
  • calc_fidelity: (Bool, default: false) Do you want to monitor the fidelity while training the NADE?
  • target: The target quantum state. If calc_fidelity=true, this is required (of course!)
  • calc_observable: (Bool, default: false) Do you want to monitor an observable while training the NADE?
  • num_samples: (integer, default: nothing) if calc_observable=true, then we need to know how many samples you want to generate from the NADE to calculate your observable on.
  • observable: (function, default: nothing, returns: the value of the observable on one sample) This is a user-specified function that calculates the value of an observable given one sample from the NADE.
  • log_every: the frequeny (in epochs) that one wishes to monitor their training metric (fidelity or an observable)
  • early_stopping: (function, default: nothing, returns: Bool) This is a user-specified function that defines a learning criteria for the NADE that, once met during the training, stops the training early (i.e. before the last epoch). The arguments to this function must be: the "current" metric value (e.g. if you're calculating fidelity, you must input the current fidelity in the training process) and other arguments required (see early_stopping_args)
  • early_stopping_args: Other required arguments required for the early_stopping function.

If you're at all confused, see run.jl for an example of how to train a NADE.