Quantum-Inspired Optimization for Constraint Satisfaction Problems

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

Quantum-inspired optimization aims to accelerate the solution of constraint satisfaction problems (CSPs) by incorporating principles of quantum mechanics. This paper explores the application of quantum phenomena, specifically superposition and entanglement, to a novel optimization algorithm designed to enhance the efficiency of CSP solving. We present a framework that leverages the inherent parallelism and non-linearity of quantum systems to provide a computational advantage over traditional methods. The core mechanism involves a quantum-inspired search strategy that adapts to the problem's structure, allowing for more effective exploration of the solution space. The research highlights the potential of quantum-inspired optimization to significantly improve the performance of CSP solvers, particularly in large-scale and complex instances.

DOI: 10.5281/zenodo.22249138

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