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)[source]¶

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.

See also

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.

Note

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.)

Parameters

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)[source]¶

Parameters

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`
`get_support_level`()	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_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.
`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()[source]¶: 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.

Parameters

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

Returns

the gradient computed

Return type

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)[source]¶

Perform optimization.

Parameters

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.

Returns

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

Raises

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.

Parameters: 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.

Parameters

function (func) – the target function
args (tuple) – the args to be injected

Returns

wrapper

Return type

function_wrapper