Work

Stochastic gradient line Bayesian optimization for efficient noise-robust optimization of parameterized quantum circuits

Year: 2022

Type: article

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.

Source: npj Quantum Information

DOI: 10.1038/s41534-022-00592-6

Authors: Shiro Tamiya, Hayata Yamasaki

Institutions: The University of Tokyo, Austrian Academy of Sciences, TU Wien

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,790

Funders: Japan Society for the Promotion of Science, Precursory Research for Embryonic Science and Technology, Moonshot Research and Development Program