Title: Quantum Entanglement-Driven Constraint Satisfaction

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum entanglement, a peculiar phenomenon in quantum mechanics, offers a unique potential for optimization algorithms. This paper explores the application of entanglement to constraint satisfaction problems (CSPs) to generate solution diversity, addressing a key limitation of classical methods. We propose a novel constrained optimization framework that leverages quantum-inspired constraint representation and entanglement-induced correlations to overcome the pitfalls of local optima. We detail the design of our algorithm, including the quantum-inspired constraint representation and the entanglement-based exploration strategy. The core claim is that this approach significantly enhances solution diversity compared to existing methods, particularly in complex, multi-objective CSPs. This work contributes to the field of constrained optimization by investigating a fundamentally new approach to mitigate local optima, potentially opening new avenues for creative problem-solving.

DOI: 10.5281/zenodo.22264721

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