Optimizers

C1 - Optimal control

Object that deals with the open loop optimal control.

class c3.optimizers.c1.C1(dir_path, fid_func, fid_subspace, pmap, callback_fids=[], algorithm=None, store_unitaries=False, options={}, run_name=None)

Bases: c3.optimizers.optimizer.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

• opt_gates (list) – Identifiers of gate to be optimized, a subset of the full gateset

• callback_fids (list of callable) – Additional fidelity function to be evaluated and stored for reference

• algorithm (callable) – From the algorithm library

• plot_dynamics (boolean) – Save plots of time-resolved dynamics in dir_path

• plot_pulses (boolean) – Save plots of control signals

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

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

• update_model (boolean) – Include the model in the optimization process

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

goal_run(current_params: tensorflow.python.framework.ops.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

include_model()

load_model_parameters(adjust_exp: str) → None

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.

C2 - Calibration

Object that deals with the closed loop optimal control.

class c3.optimizers.c2.C2(dir_path, eval_func, pmap, algorithm, 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

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)

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.c3.C3(dir_path, sampling, batch_sizes, pmap, 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

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)

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

Peroms the model learning by minimizing the figure of merit.

log_setup()

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

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

read_data(datafiles) → 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.Optimizer(pmap, algorithm=None, store_unitaries=False)

Bases: object

General optimizer class from which specific classes are inherited.

Parameters

• algorithm (callable) – From the algorithm library

• plot_dynamics (boolean) – Save plots of time-resolved dynamics in dir_path

• plot_pulses (boolean) – Save plots of control signals

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

end_log() → None

Finish the log by recording current time and total runtime.

fct_to_min(x: Union[numpy.ndarray, tensorflow.python.ops.variables.Variable]) → Union[numpy.ndarray, tensorflow.python.ops.variables.Variable]

Wrapper for the goal function.

Parameters

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

Returns

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

Return type

[float, tf.Variable]

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_with_grad(current_params)

load_best(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

log_best_unitary() → None

Save the best unitary in the log.

log_parameters() → 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_created_by(config) → None

Store the config file location used to created this optimizer.

set_exp(exp) → None

start_log() → None

Initialize the log with current time.

Sensitivity analysis

Object that deals with the sensitivity test.

class c3.optimizers.sensitivity.SET(dir_path, fom, estimator_list, sampling, batch_sizes, pmap, state_labels=None, sweep_map=None, sweep_bounds=None, algorithm=None, run_name=None, same_dyn=False, options={})

Bases: c3.optimizers.optimizer.Optimizer

Object that deals with the sensitivity test.

Parameters

• dir_path (str) – Filepath to save results

• fom (callable) – Figure of merit

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

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

• sweep_map (list) – Identifiers to be swept

• state_labels (list) – Identifiers for the qubit subspaces

• algorithm (callable) – From the algorithm library

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

• same_dyn (boolean) –

  ?

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

goal_run(current_params)

Evaluate the figure of merit for the current model parameters.

Parameters

val (tf.Tensor) – Current model parameters

Returns

Figure of merit

Return type

tf.float64

log_setup(dir_path, run_name) → None

Create the folders to store data.

Parameters

• dir_path (str) – Filepath

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

read_data(datafiles)

Open data files and read in experiment results.

Parameters

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

select_from_data(batch_size)

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

sensitivity()

Run the sensitivity analysis.

Module contents