Quantum-Accelerated Constraint Satisfaction for Large-Scale Problems

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of quantum computing to significantly accelerate constraint satisfaction problems (CSPs) on large-scale datasets. Current CSP solvers often struggle with scalability due to computational complexity. We propose a novel quantum algorithm for CSP solving, focusing on quantum parallelism to enhance search and constraint evaluation. We demonstrate the effectiveness of this approach through a theoretical analysis and preliminary experiments on a benchmark CSP dataset. The core claim is that quantum-accelerated CSP solvers can offer a substantial improvement in performance compared to classical methods, particularly for large-scale problems.

DOI: 10.5281/zenodo.22272563

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