Quantum-Inspired Algorithms for Constraint Satisfaction Problems

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Constraint satisfaction problems (CSPs) are ubiquitous in various domains, including artificial intelligence, scheduling, and optimization. Traditional CSP solvers often struggle with large and complex problems, demanding significant computational resources. This paper investigates the potential of quantum-inspired algorithms to accelerate the solution of CSPs. We propose an algorithm that utilizes concepts reminiscent of quantum mechanics, specifically amplitude amplification and a modified annealing process, to efficiently explore the solution space. The core claim is that by intelligently mimicking quantum phenomena on classical computers, substantial speedups can be achieved compared to conventional CSP solvers. Our approach focuses on the direct application of quantum-inspired techniques without relying on the availability of quantum hardware, providing a viable strategy for tackling computationally demanding CSP instances. The algorithm's performance is evaluated theoretically, with a focus on the key aspects of search space reduction and convergence rates. The results suggest a promising direction for future research in hybrid quantum-classical algorithms for CSPs.

DOI: 10.5281/zenodo.22316479

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