Quantum-Inspired Optimization for Constraint Satisfaction

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper investigates the application of quantum-inspired optimization techniques to constraint satisfaction problems (CSPs). CSPs are a ubiquitous class of problems in various fields, including scheduling, resource allocation, and game theory, where the goal is to find a satisfying assignment of variables to elements while adhering to constraints. Traditional optimization methods often struggle with the inherent complexity of CSPs, particularly when dealing with large instances and intricate constraints. We propose a novel approach that leverages quantum-inspired algorithms to guide the search process, potentially improving solver efficiency and robustness. Quantum concepts are introduced as a means to enhance the exploration of the solution space and mitigate local optima. This work explores the potential benefits of integrating quantum-inspired methods into the CSP solving process.

DOI: 10.5281/zenodo.22200716

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