Stochastic Gradient Line Bayesian Optimization for Efficient Noise-Robust Optimization of Parameterized Quantum Circuits

TL;DR

This paper introduces SGLBO, an efficient optimization algorithm for parameterized quantum circuits that significantly reduces measurement costs and enhances noise robustness by combining stochastic gradient descent with Bayesian optimization.

Contribution

The paper presents SGLBO, a novel method that integrates stochastic gradient descent and Bayesian optimization to optimize quantum circuits more efficiently and robustly against noise.

Findings

Reduces measurement-shot cost drastically

Improves optimization accuracy

Enhances noise robustness in quantum circuit optimization

Abstract

Optimizing parameterized quantum circuits is a key routine in using near-term quantum devices. However, the existing algorithms for such optimization require an excessive number of quantum-measurement shots for estimating expectation values of observables and repeating many iterations, whose cost has been a critical obstacle for practical use. We develop an efficient alternative optimization algorithm, stochastic gradient line Bayesian optimization (SGLBO), to address this problem. SGLBO reduces the measurement-shot cost by estimating an appropriate direction of updating circuit parameters based on stochastic gradient descent (SGD) and further utilizing Bayesian optimization (BO) to estimate the optimal step size for each iteration in SGD. In addition, we formulate an adaptive measurement-shot strategy and introduce a technique of suffix averaging to reduce the effect of statistical and hardware noise. Our numerical simulation demonstrates that the SGLBO augmented with these techniques can drastically reduce the measurement-shot cost, improve the accuracy, and make the optimization noise-robust.