Optimizers

C1 - Optimal control

Object that deals with the open loop optimal control.

exception c3.optimizers.optimalcontrol.OptResultOOBError

Bases: Exception

class c3.optimizers.optimalcontrol.OptimalControl(fid_func, fid_subspace, pmap, dir_path=None, callback_fids=None, algorithm=None, initial_point: str = '', store_unitaries=False, options={}, run_name=None, interactive=True, include_model=False, logger=None, fid_func_kwargs={}, ode_solver=None, ode_step_function='schrodinger', only_final_state=False)

Bases: Optimizer

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

• fid_func_kwargs (dict) – Additional kwargs to be passed to the main fidelity function.

goal_run(current_params: Tensor) → tf.float64

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

goal_run_ode(current_params: Tensor) → tf.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: Tensor) → tf.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

load_model_parameters(adjust_exp: str) → None

log_setup() → None

Create the folders to store data.

optimize_controls(setup_log: bool = True) → None

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

set_callback_fids(callback_fids) → None

set_fid_func(fid_func) → None

set_goal_function(ode_solver=None, ode_step_function='schrodinger', only_final_state=False)

C2 - Calibration

Object that deals with the closed loop optimal control.

class c3.optimizers.calibration.Calibration(eval_func, pmap, algorithm, dir_path=None, exp_type=None, exp_right=None, options={}, run_name=None, logger: Optional[List] = None)

Bases: 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

goal_run(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(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

log_setup() → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

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

optimize_controls() → None

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

set_eval_func(eval_func, exp_type)

Setter for the eval function.

Parameters

eval_func (callable) – Function to be evaluated

C3 - Characterization

Object that deals with the model learning.

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={}, logger: Optional[List] = None)

Bases: 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

confirm() → 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(current_params: constant) → tf.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(current_params)

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

learn_model() → None

Performs the model learning by minimizing the figure of merit.

log_setup() → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

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

read_data(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(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

Optimizer module

Optimizer object, where the optimal control is done.

class c3.optimizers.optimizer.BaseLogger

Bases: object

end_log(opt, logdir)

log_parameters(evaluation, optim_status)

start_log(opt, logdir)

class c3.optimizers.optimizer.BestPointLogger

Bases: BaseLogger

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

Bases: object

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

end_log() → None

Finish the log by recording current time and total runtime.

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

goal_run(current_params: Union[ndarray, constant]) → Union[ndarray, constant]

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

goal_run_with_grad(current_params)

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.

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.

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

replace_logdir(new_logdir)

Specify a new filepath to store the log.

Parameters

new_logdir –

set_algorithm(algorithm: Optional[Callable]) → None

set_created_by(config) → None

Store the config file location used to created this optimizer.

set_exp(exp: Experiment) → None

start_log() → None

Initialize the log with current time.

class c3.optimizers.optimizer.TensorBoardLogger

Bases: BaseLogger

log_parameters(evaluation, optim_status)

set_logdir(logdir)

start_log(opt, logdir)

write_params(params, step=0)

Sensitivity analysis

Module for Sensitivity Analysis. This allows the sweeping of the goal function in a given range of parameters to ascertain whether the dataset being used is sensitive to changes in the parameters of interest

class c3.optimizers.sensitivity.Sensitivity(sampling: str, batch_sizes: Dict[str, int], pmap: ParameterMap, datafiles: Dict[str, str], state_labels: Dict[str, List[Any]], sweep_map: List[List[Tuple[str]]], sweep_bounds: List[List[int]], algorithm: str, estimator: Optional[str] = None, estimator_list: Optional[List[str]] = None, dir_path: Optional[str] = None, run_name: Optional[str] = None, options={})

Bases: ModelLearning

Class for Sensitivity Analysis, subclassed from Model Learning

Parameters

• sampling (str) – Name of the sampling method from library

• batch_sizes (Dict[str, int]) – Number of points to select from the dataset

• pmap (ParameterMap) – Model parameter map

• datafiles (Dict[str, str]) – The datafiles for each of the learning datasets

• state_labels (Dict[str, List[Any]]) – The labels for the excited states of the system

• sweep_map (List[List[List[str]]]) – Map of variables to be swept in exp_opt_map format

• sweep_bounds (List[List[int]]) – List of upper and lower bounds for each sweeping variable

• algorithm (str) – Name of the sweeping algorithm from the library

• estimator (str, optional) – Name of estimator method from library, by default None

• estimator_list (List[str], optional) – List of different estimators to be used, by default None

• dir_path (str, optional) – Path to save sensitivity logs, by default None

• run_name (str, optional) – Name of the experiment run, by default None

• options (dict, optional) – Options for the sweeping algorithm, by default {}

Raises

NotImplementedError – When trying to set the estimator or estimator_list

sensitivity()

Run the sensitivity analysis.

Module contents