Quantum-Inspired Optimization for Large-Scale Constraint Satisfaction Problems

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Large-Scale Constraint Satisfaction Problems (LSCsPS) represent a significant challenge in computational science due to their inherent complexity and the exponential growth of the search space. Traditional optimization techniques often struggle to find optimal or even near-optimal solutions within reasonable timeframes. This paper introduces a novel quantum-inspired optimization algorithm designed to accelerate the solution of LSCsPS. The core idea is to mimic key principles from quantum mechanics, namely superposition and entanglement, to facilitate more efficient exploration of the solution space. The proposed algorithm utilizes probabilistic representations of variables and employs entanglement-inspired search strategies to navigate the complex solution landscape. We present a detailed formulation of the algorithm and analyze its potential benefits in tackling LSCsPS. The approach aims to overcome limitations of classical methods by leveraging the inherent parallelism and non-deterministic nature of quantum systems, although without explicitly employing quantum hardware. The algorithm's performance is evaluated through theoretical analysis and conceptual demonstrations, highlighting its promise as a viable approach for addressing this important class of problems.

DOI: 10.5281/zenodo.22220078

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