qiskit.aqua.components.optimizers.SPSA¶

class SPSA(maxiter=1000, save_steps=1, last_avg=1, c0=0.6283185307179586, c1=0.1, c2=0.602, c3=0.101, c4=0, skip_calibration=False, max_trials=None)[código fonte]

Simultaneous Perturbation Stochastic Approximation (SPSA) optimizer.

SPSA is an algorithmic method for optimizing systems with multiple unknown parameters. As an optimization method, it is appropriately suited to large-scale population models, adaptive modeling, and simulation optimization.

Ver também

Many examples are presented at the SPSA Web site.

SPSA is a descent method capable of finding global minima, sharing this property with other methods as simulated annealing. Its main feature is the gradient approximation, which requires only two measurements of the objective function, regardless of the dimension of the optimization problem.

Nota

SPSA can be used in the presence of noise, and it is therefore indicated in situations involving measurement uncertainty on a quantum computation when finding a minimum. If you are executing a variational algorithm using a Quantum ASseMbly Language (QASM) simulator or a real device, SPSA would be the most recommended choice among the optimizers provided here.

The optimization process includes a calibration phase, which requires additional functional evaluations.

For further details, please refer to https://arxiv.org/pdf/1704.05018v2.pdf#section*.11 (Supplementary information Section IV.)

Parâmetros
• maxiter (int) – Maximum number of iterations to perform.

• save_steps (int) – Save intermediate info every save_steps step. It has a min. value of 1.

• last_avg (int) – Averaged parameters over the last_avg iterations. If last_avg = 1, only the last iteration is considered. It has a min. value of 1.

• c0 (float) – The initial a. Step size to update parameters.

• c1 (float) – The initial c. The step size used to approximate gradient.

• c2 (float) – The alpha in the paper, and it is used to adjust a (c0) at each iteration.

• c3 (float) – The gamma in the paper, and it is used to adjust c (c1) at each iteration.

• c4 (float) – The parameter used to control a as well.

• skip_calibration (bool) – Skip calibration and use provided c(s) as is.

• max_trials (Optional[int]) – Deprecated, use maxiter.

__init__(maxiter=1000, save_steps=1, last_avg=1, c0=0.6283185307179586, c1=0.1, c2=0.602, c3=0.101, c4=0, skip_calibration=False, max_trials=None)[código fonte]
Parâmetros
• maxiter (int) – Maximum number of iterations to perform.

• save_steps (int) – Save intermediate info every save_steps step. It has a min. value of 1.

• last_avg (int) – Averaged parameters over the last_avg iterations. If last_avg = 1, only the last iteration is considered. It has a min. value of 1.

• c0 (float) – The initial a. Step size to update parameters.

• c1 (float) – The initial c. The step size used to approximate gradient.

• c2 (float) – The alpha in the paper, and it is used to adjust a (c0) at each iteration.

• c3 (float) – The gamma in the paper, and it is used to adjust c (c1) at each iteration.

• c4 (float) – The parameter used to control a as well.

• skip_calibration (bool) – Skip calibration and use provided c(s) as is.

• max_trials (Optional[int]) – Deprecated, use maxiter.

Methods

 __init__([maxiter, save_steps, last_avg, …]) type maxiter int return support level dictionary gradient_num_diff(x_center, f, epsilon[, …]) We compute the gradient with the numeric differentiation in the parallel way, around the point x_center. optimize(num_vars, objective_function[, …]) Perform optimization. Print algorithm-specific options. Set max evals grouped set_options(**kwargs) Sets or updates values in the options dictionary. wrap_function(function, args) Wrap the function to implicitly inject the args at the call of the function.

Attributes

 bounds_support_level Returns bounds support level gradient_support_level Returns gradient support level initial_point_support_level Returns initial point support level is_bounds_ignored Returns is bounds ignored is_bounds_required Returns is bounds required is_bounds_supported Returns is bounds supported is_gradient_ignored Returns is gradient ignored is_gradient_required Returns is gradient required is_gradient_supported Returns is gradient supported is_initial_point_ignored Returns is initial point ignored is_initial_point_required Returns is initial point required is_initial_point_supported Returns is initial point supported setting Return setting
property bounds_support_level

Returns bounds support level

get_support_level()[código fonte]

return support level dictionary

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parâmetros
• x_center (ndarray) – point around which we compute the gradient

• f (func) – the function of which the gradient is to be computed.

• epsilon (float) – the epsilon used in the numeric differentiation.

• max_evals_grouped (int) – max evals grouped

Retorna

the gradient computed

Tipo de retorno

grad

property gradient_support_level

Returns gradient support level

property initial_point_support_level

Returns initial point support level

property is_bounds_ignored

Returns is bounds ignored

property is_bounds_required

Returns is bounds required

property is_bounds_supported

Returns is bounds supported

property is_gradient_ignored

Returns is gradient ignored

property is_gradient_required

Returns is gradient required

property is_gradient_supported

Returns is gradient supported

property is_initial_point_ignored

Returns is initial point ignored

property is_initial_point_required

Returns is initial point required

property is_initial_point_supported

Returns is initial point supported

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)[código fonte]

Perform optimization.

Parâmetros
• num_vars (int) – Number of parameters to be optimized.

• objective_function (callable) – A function that computes the objective function.

• gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.

• variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.

• initial_point (numpy.ndarray[float]) – Initial point.

Retorna

point, value, nfev

point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Levanta

ValueError – invalid input

print_options()

Print algorithm-specific options.

set_max_evals_grouped(limit)

Set max evals grouped

set_options(**kwargs)

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parâmetros

kwargs (dict) – options, given as name=value.

property setting

Return setting

static wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parâmetros
• function (func) – the target function

• args (tuple) – the args to be injected

Retorna

wrapper

Tipo de retorno

function_wrapper