c3.optimizers.optimizer

Optimizer object, where the optimal control is done.

Module Contents

class c3.optimizers.optimizer.Optimizer(pmap: c3.parametermap.ParameterMap, initial_point: str = '', algorithm: Callable = None, store_unitaries: bool = False, logger: List = None)

General optimizer class from which specific classes are inherited.

Parameters

• algorithm (callable) – From the algorithm library

• store_unitaries (boolean) – Store propagators as text and pickle

• logger (List) – Logging classes

replace_logdir(new_logdir)

Specify a new filepath to store the log.

Parameters

new_logdir –

set_created_by(config) → None

Store the config file location used to created this optimizer.

load_best(init_point, extend_bounds=False) → 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

• extend_bounds (bool) – Whether or not to allow the loaded optimal parameters’ bounds to be extended if they exceed those specified.

start_log() → None

Initialize the log with current time.

end_log() → None

Finish the log by recording current time and total runtime.

log_best_unitary() → None

Save the best unitary in the log.

log_parameters(params) → None

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

abstract goal_run(current_params: Union[numpy.ndarray, tensorflow.constant]) → Union[numpy.ndarray, tensorflow.constant]

Placeholder for the goal function. To be implemented by inherited classes.

lookup_gradient(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(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(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.