Surrogate-based optimization for variational quantum algorithms

TL;DR

This paper proposes using surrogate models based on kernel approximations to improve the classical optimization step in variational quantum algorithms, leading to better performance in quantum approximate optimization and molecular ground state preparation.

Contribution

Introduces a novel surrogate modeling approach for variational quantum algorithms, enhancing classical optimization with fewer measurements and noise resilience.

Findings

Surrogate models outperform traditional optimization methods in variational algorithms.

Kernel-based surrogate models efficiently reconstruct local cost function patches.

Improved optimization results in quantum approximate optimization and molecular ground states.

Abstract

Variational quantum algorithms are a class of techniques intended to be used on near-term quantum computers. The goal of these algorithms is to perform large quantum computations by breaking the problem down into a large number of shallow quantum circuits, complemented by classical optimization and feedback between each circuit execution. One path for improving the performance of these algorithms is to enhance the classical optimization technique. Given the relative ease and abundance of classical computing resources, there is ample opportunity to do so. In this work, we introduce the idea of learning surrogate models for variational circuits using few experimental measurements, and then performing parameter optimization using these models as opposed to the original data. We demonstrate this idea using a surrogate model based on kernel approximations, through which we reconstruct local patches of variational cost functions using batches of noisy quantum circuit results. Through application to the quantum approximate optimization algorithm and preparation of ground states for molecules, we demonstrate the superiority of surrogate-based optimization over commonly-used optimization techniques for variational algorithms.