c3.optimizers.modellearning

Object that deals with the model learning.

Module Contents

class c3.optimizers.modellearning.ModelLearning(sampling, batch_sizes, pmap, datafiles, dir_path=None, estimator=None, seqs_per_point=None, state_labels=None, callback_foms=[], algorithm=None, run_name=None, options={})

Bases: c3.optimizers.optimizer.Optimizer

Object that deals with the model learning.

Parameters

• dir_path (str) – Filepath to save results

• sampling (str) – Sampling method from the sampling library

• batch_sizes (list) – Number of points to select from each dataset

• seqs_per_point (int) – Number of sequences that use the same parameter set

• pmap (ParameterMap) – Identifiers for the parameter vector

• state_labels (list) – Identifiers for the qubit subspaces

• callback_foms (list) – Figures of merit to additionally compute and store

• algorithm (callable) – From the algorithm library

• run_name (str) – User specified name for the run, will be used as root folder

• options (dict) – Options to be passed to the algorithm

log_setup(self) → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

• run_name (str) – User specified name for the run

read_data(self, datafiles: Dict[str, str]) → None

Open data files and read in experiment results.

Parameters

datafiles (dict) – List of paths for files that contain learning data.

select_from_data(self, batch_size) → List[int]

Select a subset of each dataset to compute the goal function on.

Parameters

batch_size (int) – Number of points to select

Returns

Indeces of the selected data points.

Return type

list

learn_model(self) → None

Performs the model learning by minimizing the figure of merit.

confirm(self) → None

Compute the validation set, i.e. the value of the goal function on all points of the dataset that were not used for learning.

goal_run(self, current_params: tensorflow.constant) → tensorflow.float64

Evaluate the figure of merit for the current model parameters.

Parameters

current_params (tf.Tensor) – Current model parameters

Returns

Figure of merit

Return type

tf.float64

goal_run_with_grad(self, current_params)

Same as goal_run but with gradient. Very resource intensive. Unoptimized at the moment.

replace_logdir(self, new_logdir)

Specify a new filepath to store the log.

Parameters

new_logdir –

set_created_by(self, config) → None

Store the config file location used to created this optimizer.

load_best(self, init_point) → None

Load a previous parameter point to start the optimization from. Legacy wrapper. Method moved to Parametermap.

Parameters

init_point (str) – File location of the initial point

start_log(self) → None

Initialize the log with current time.

end_log(self) → None

Finish the log by recording current time and total runtime.

log_best_unitary(self) → None

Save the best unitary in the log.

log_parameters(self) → None

Log the current status. Write parameters to log. Update the current best parameters. Call plotting functions as set up.

lookup_gradient(self, x)

Return the stored gradient for a given parameter set.

Parameters

x (np.array) – Parameter set.

Returns

Value of the gradient.

Return type

np.array

fct_to_min(self, input_parameters: Union[numpy.ndarray, tensorflow.constant]) → Union[numpy.ndarray, tensorflow.constant]

Wrapper for the goal function.

Parameters

input_parameters ([np.array, tf.constant]) – Vector of parameters in the optimizer friendly way.

Returns

Value of the goal function. Float if input is np.array else tf.constant

Return type

[np.ndarray, tf.constant]

fct_to_min_autograd(self, x)

Wrapper for the goal function, including evaluation and storage of the gradient.

Parameters

xnp.array

Vector of parameters in the optimizer friendly way.

float

Value of the goal function.