c3.optimizers.calibration

Object that deals with the closed loop optimal control.

Module Contents

class c3.optimizers.calibration.Calibration(eval_func, pmap, algorithm, dir_path=None, exp_type=None, exp_right=None, options={}, run_name=None)

Bases: c3.optimizers.optimizer.Optimizer

Object that deals with the closed loop optimal control.

Parameters

• dir_path (str) – Filepath to save results

• eval_func (callable) – infidelity function to be minimized

• pmap (ParameterMap) – Identifiers for the parameter vector

• algorithm (callable) – From the algorithm library

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

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

set_eval_func(self, eval_func, exp_type)

Setter for the eval function.

Parameters

eval_func (callable) – Function to be evaluated

log_setup(self) → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

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

optimize_controls(self) → None

Apply a search algorithm to your gateset given a fidelity function.

goal_run(self, current_params)

Evaluate the goal function for current parameters.

Parameters

current_params (tf.Tensor) – Vector representing the current parameter values.

Returns

Value of the goal function

Return type

tf.float64

log_pickle(self, params, seqs, results, results_std, shots)

Save a pickled version of the performed experiment, suitable for model learning.

Parameters

• params (tf.Tensor) – Vector of parameter values

• seqs (list) – Strings identifying the performed instructions

• results (list) – Values of the goal function

• results_std (list) – Standard deviation of the results, in the case of noisy data

• shots (list) – Number of repetitions used in averaging noisy data

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.