Quantum-Enhanced Constraint Satisfaction with Bayesian Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum-enhanced constraint satisfaction problems (CSPs) offer the potential for significant speedups compared to classical approaches, particularly for large and complex CSPs. This paper investigates a novel quantum-enhanced strategy for solving CSPs, utilizing a quantum annealer to efficiently explore the solution space and identify optimal configurations. We introduce a Bayesian optimization framework tailored for this application, integrating quantum annealing to accelerate the search process and improve solution quality. The core mechanism centers on leveraging quantum annealing to identify promising regions of the search space, guided by Bayesian optimization, resulting in significantly reduced computational cost and enhanced solution accuracy. The paper details the implementation of the quantum annealer, Bayesian optimization algorithm, and the resulting performance evaluation on a benchmark CSP. The results demonstrate a substantial improvement in solution finding time and solution quality compared to classical approaches, highlighting the potential of this quantum-enhanced strategy.

DOI: 10.5281/zenodo.22265021

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