# 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]

Bases : qiskit.aqua.components.optimizers.optimizer.Optimizer

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.

Voir aussi

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.)

Paramètres
• 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

 get_support_level return support level dictionary gradient_num_diff We compute the gradient with the numeric differentiation in the parallel way, around the point x_center. optimize Perform optimization. print_options Print algorithm-specific options. set_max_evals_grouped Set max evals grouped set_options Sets or updates values in the options dictionary. wrap_function 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

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

is_gradient_required

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