`c3.optimizers.optimalcontrol_robust`¶

Module Contents¶

class c3.optimizers.optimalcontrol_robust.OptimalControlRobust(noise_map, **kwargs)[source]¶

Object that deals with the open loop optimal control.

Parameters

dir_path (str) – Filepath to save results
fid_func (callable) – infidelity function to be minimized
fid_subspace (list) – Indeces identifying the subspace to be compared
pmap (ParameterMap) – Identifiers for the parameter vector
callback_fids (list of callable) – Additional fidelity function to be evaluated and stored for reference
algorithm (callable) – From the algorithm library Save plots of control signals
store_unitaries (boolean) – Store propagators as text and pickle
options (dict) – Options to be passed to the algorithm
run_name (str) – User specified name for the run, will be used as root folder

goal_run(current_params: tensorflow.Tensor) → tensorflow.float64[source]¶

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

optimize_controls(setup_log: bool = True) → None¶: Apply a search algorithm to your gateset given a fidelity function.

goal_run_ode(current_params: tensorflow.Tensor) → tensorflow.float64¶

Evaluate the goal function using ode solver for current parameters.

Parameters: current_params (tf.Tensor) – Vector representing the current parameter values.
Returns: Value of the goal function
Return type: tf.float64

goal_run_ode_only_final(current_params: tensorflow.Tensor) → tensorflow.float64¶

Evaluate the goal function using ode solver for current parameters.

Parameters: current_params (tf.Tensor) – Vector representing the current parameter values.
Returns: Value of the goal function
Return type: tf.float64

replace_logdir(new_logdir)¶

Specify a new filepath to store the log.

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.

end_log() → None¶: Finish the log by recording current time and total runtime.

log_parameters(params) → None¶: Log the current status. Write parameters to log. Update the current best parameters. Call plotting functions as set up.

lookup_gradient(x)¶

Return the stored gradient for a given parameter set.

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