QNSPSA

class QNSPSA(fidelity, maxiter=100, blocking=True, allowed_increase=None, learning_rate=None, perturbation=None, resamplings=1, perturbation_dims=None, regularization=None, hessian_delay=0, lse_solver=None, initial_hessian=None, callback=None, termination_checker=None)

Bases: qiskit.algorithms.optimizers.spsa.SPSA

The Quantum Natural SPSA (QN-SPSA) optimizer.

The QN-SPSA optimizer [1] is a stochastic optimizer that belongs to the family of gradient descent methods. This optimizer is based on SPSA but attempts to improve the convergence by sampling the natural gradient instead of the vanilla, first-order gradient. It achieves this by approximating Hessian of the fidelity of the ansatz circuit.

Compared to natural gradients, which require $\mathcal{O}(d^2)$ expectation value evaluations for a circuit with $d$ parameters, QN-SPSA only requires $\mathcal{O}(1)$ and can therefore significantly speed up the natural gradient calculation by sacrificing some accuracy. Compared to SPSA, QN-SPSA requires 4 additional function evaluations of the fidelity.

The stochastic approximation of the natural gradient can be systematically improved by increasing the number of resamplings. This leads to a Monte Carlo-style convergence to the exact, analytic value.

Examples

This short example runs QN-SPSA for the ground state calculation of the Z ^ Z observable where the ansatz is a PauliTwoDesign circuit.

import numpy as np
from qiskit.algorithms.optimizers import QNSPSA
from qiskit.circuit.library import PauliTwoDesign
from qiskit.primitives import Estimator, Sampler
from qiskit.quantum_info import Pauli
 
# problem setup
ansatz = PauliTwoDesign(2, reps=1, seed=2)
observable = Pauli("ZZ")
initial_point = np.random.random(ansatz.num_parameters)
 
# loss function
estimator = Estimator()
 
def loss(x):
    result = estimator.run([ansatz], [observable], [x]).result()
    return np.real(result.values[0])
 
# fidelity for estimation of the geometric tensor
sampler = Sampler()
fidelity = QNSPSA.get_fidelity(ansatz, sampler)
 
# run QN-SPSA
qnspsa = QNSPSA(fidelity, maxiter=300)
result = qnspsa.optimize(ansatz.num_parameters, loss, initial_point=initial_point)

This is a legacy version solving the same problem but using Qiskit Opflow instead of the Qiskit Primitives. Note however, that this usage is pending deprecation.

import numpy as np
from qiskit.algorithms.optimizers import QNSPSA
from qiskit.circuit.library import PauliTwoDesign
from qiskit.opflow import Z, StateFn
 
ansatz = PauliTwoDesign(2, reps=1, seed=2)
observable = Z ^ Z
initial_point = np.random.random(ansatz.num_parameters)
 
def loss(x):
    bound = ansatz.bind_parameters(x)
    return np.real((StateFn(observable, is_measurement=True) @ StateFn(bound)).eval())
 
fidelity = QNSPSA.get_fidelity(ansatz)
qnspsa = QNSPSA(fidelity, maxiter=300)
result = qnspsa.optimize(ansatz.num_parameters, loss, initial_point=initial_point)

References

[1] J. Gacon et al, “Simultaneous Perturbation Stochastic Approximation of the Quantum Fisher Information”, arXiv:2103.09232

Parameters

• fidelity (FIDELITY) – A function to compute the fidelity of the ansatz state with itself for two different sets of parameters.
• maxiter (int) – The maximum number of iterations. Note that this is not the maximal number of function evaluations.
• blocking (bool) – If True, only accepts updates that improve the loss (up to some allowed increase, see next argument).
• allowed_increase (float | None) – If blocking is True, this argument determines by how much the loss can increase with the proposed parameters and still be accepted. If None, the allowed increases is calibrated automatically to be twice the approximated standard deviation of the loss function.
• learning_rate (float | Callable[[], Iterator] | None) – The update step is the learning rate is multiplied with the gradient. If the learning rate is a float, it remains constant over the course of the optimization. It can also be a callable returning an iterator which yields the learning rates for each optimization step. If learning_rate is set perturbation must also be provided.
• perturbation (float | Callable[[], Iterator] | None) – Specifies the magnitude of the perturbation for the finite difference approximation of the gradients. Can be either a float or a generator yielding the perturbation magnitudes per step. If perturbation is set learning_rate must also be provided.
• resamplings (int | dict[int, int]) – The number of times the gradient (and Hessian) is sampled using a random direction to construct a gradient estimate. Per default the gradient is estimated using only one random direction. If an integer, all iterations use the same number of resamplings. If a dictionary, this is interpreted as {iteration: number of resamplings per iteration}.
• perturbation_dims (int | None) – The number of perturbed dimensions. Per default, all dimensions are perturbed, but a smaller, fixed number can be perturbed. If set, the perturbed dimensions are chosen uniformly at random.
• regularization (float | None) – To ensure the preconditioner is symmetric and positive definite, the identity times a small coefficient is added to it. This generator yields that coefficient.
• hessian_delay (int) – Start multiplying the gradient with the inverse Hessian only after a certain number of iterations. The Hessian is still evaluated and therefore this argument can be useful to first get a stable average over the last iterations before using it as preconditioner.
• lse_solver (Callable[[np.ndarray, np.ndarray], np.ndarray] | None) – The method to solve for the inverse of the Hessian. Per default an exact LSE solver is used, but can e.g. be overwritten by a minimization routine.
• initial_hessian (np.ndarray | None) – The initial guess for the Hessian. By default the identity matrix is used.
• callback (CALLBACK | None) – A callback function passed information in each iteration step. The information is, in this order: the parameters, the function value, the number of function evaluations, the stepsize, whether the step was accepted.
• termination_checker (TERMINATIONCHECKER | None) – A callback function executed at the end of each iteration step. The arguments are, in this order: the parameters, the function value, the number of function evaluations, the stepsize, whether the step was accepted. If the callback returns True, the optimization is terminated. To prevent additional evaluations of the objective method, if the objective has not yet been evaluated, the objective is estimated by taking the mean of the objective evaluations used in the estimate of the gradient.

Methods

calibrate

estimate_stddev

get_fidelity

get_support_level

gradient_num_diff

minimize

optimize

print_options

set_max_evals_grouped

set_options

wrap_function

Attributes

bounds_support_level

gradient_support_level

initial_point_support_level

is_bounds_ignored

is_bounds_required

is_bounds_supported

is_gradient_ignored

is_gradient_required

is_gradient_supported

is_initial_point_ignored

is_initial_point_required

is_initial_point_supported

setting

settings