Work

Gaussian process model kernels for noisy optimization in variational quantum algorithms

Year: 2026

Type: article

Abstract: Variational quantum algorithms (VQAs) aim at solving classical or quantum optimization problems by optimizing parametrized trial states on a quantum device, based on the outcomes of noisy projective measurements. The associated optimization process benefits from an accurate modeling of the cost function landscape using Gaussian process models (GPMs), whose performance is critically affected by the choice of their kernel. Here, we introduce specialized trigonometric kernels, inspired by the observation that typical VQA cost functions display oscillatory behavior with only few frequencies. Appropriate scores to benchmark the reliability of a GPM are defined, and a systematic comparison between different kernels is carried out on prototypical problems from quantum chemistry and combinatorial optimization. We further introduce RotoGP, a sequential line-search optimizer equipped with a GPM, and test how different kernels can help mitigate noise and improve optimization convergence. Overall, we observe that the trigonometric kernels show the best performance in most of the cases under study.

Source: Physical Review Research

DOI: 10.1103/p947-r9l1

Authors: L. Arceci, V. Kuzmin, R. van Bijnen

Institutions: Austrian Academy of Sciences, Universität Innsbruck, Institute for Quantum Optics and Quantum Information Innsbruck

Language: English

Topic: Quantum Computing Algorithms and Architecture

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,625

Funders: Austrian Science Fund, Österreichische Forschungsförderungsgesellschaft, Universität Innsbruck