Designing Quantum Annealing Schedules using Bayesian Optimization

TL;DR

This paper introduces a Bayesian optimization approach to design quantum annealing schedules that significantly improve fidelity and performance for various quantum models and algorithms, demonstrated through experiments.

Contribution

The paper presents a novel Bayesian optimization method for quantum annealing schedule design, reducing resource requirements and enhancing performance over standard protocols.

Findings

Bayesian optimization finds schedules with fidelities orders of magnitude better.

Improved annealing schedules enhance quantum and reverse annealing performance.

Experimental validation on a neutral atom quantum processor confirms effectiveness.

Abstract

We propose and analyze the use of Bayesian optimization techniques to design quantum annealing schedules with minimal user and resource requirements. We showcase our scheme with results for two paradigmatic spin models. We find that Bayesian optimization is able to identify schedules resulting in fidelities several orders of magnitude better than standard protocols for both quantum and reverse annealing, as applied to the $p$-spin model. We also show that our scheme can help improve the design of hybrid quantum algorithms for hard combinatorial optimization problems, such as the maximum independent set problem, and illustrate these results via experiments on a neutral atom quantum processor available on Amazon Braket.