Resource-efficient quantum approximate optimization algorithm via Bayesian optimization and maximum-probability evaluation

TL;DR

This paper introduces a resource-efficient QAOA framework that uses maximum-probability measurement outcomes and Bayesian optimization to reduce measurement shots while maintaining solution quality.

Contribution

It proposes a novel QAOA approach combining maximum-probability objectives with adaptive shot allocation and Bayesian optimization for improved efficiency.

Findings

Achieves comparable solution quality with fewer shots than expectation-based QAOA.

Effective on 3-regular MaxCut problems with both weighted and unweighted instances.

Demonstrates practical benefits of reorganizing QAOA around maximum-probability outcomes.

Abstract

The quantum approximate optimization algorithm (QAOA) is a leading variational approach to combinatorial optimization, but its practical performance depends strongly on objective design, parameter search, and shot allocation. We present a resource-efficient QAOA framework that uses the cut value of the most probable measured bitstring as the optimization objective, combines it with Bayesian optimization, and adaptively allocates shots using dual criteria based on mode confidence and normalized cut-value variance. Numerical experiments on 3-regular MaxCut show that, for both unweighted and weighted instances, the proposed scheme achieves discrete-solution quality comparable to that of the conventional expectation-based objective while typically requiring fewer total shots to reach the same final mode accuracy. These results indicate that reorganizing QAOA around the maximum-probability bitstring provides an effective route to improving practical performance under limited measurement budgets.