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Quantum Entanglement-Based Constraint Satisfaction

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

This paper investigates the potential of quantum entanglement to revolutionize constraint satisfaction algorithms, specifically focusing on improved performance and robustness compared to classical methods. We propose a novel quantum-inspired optimization algorithm predicated on the principles of quantum tunneling, aiming to leverage entanglement to navigate complex solution spaces more effectively. The core claim is that this approach offers a demonstrably faster and more reliable solution for challenging optimization problems, particularly those involving high dimensionality and intricate constraints. The research delves into the theoretical underpinnings of entanglement-based optimization, explores its potential applications, and presents preliminary results demonstrating enhanced performance in a simulated environment. The paper concludes with a discussion of future research directions and the challenges associated with translating this concept into practical implementations.

DOI
10.5281/zenodo.22198105

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Quantum-inspired optimization aims to enhance the performance of constraint satisfaction problems (CSPs) by incorporating principles of quantum mechanics. This paper investigates a novel quantum-inspired optimization technique, employing a dynamic quantum-state evolution to guide the search for optimal solutions. We introduce a framework that mimics quantum phenomena, such as superposition and entanglement, to explore the solution space more efficiently and overcome limitations of traditional optimization methods. The core mechanism leverages a quantum-inspired algorithm to dynamically adjust the search space, focusing on promising regions of the solution space. The paper details the design of the algorithm, its performance evaluation against established CSP solvers, and a thorough analysis of its effectiveness in addressing challenges associated with complex constraints. We demonstrate that this quantum-inspired approach yields significant improvements in solving specific CSP instances, offering a promising direction for future research in constraint satisfaction and optimization.
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