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Quantum-Inspired Constraint Satisfaction with Adaptive Randomness

Jincheng Zhang

Published Aug 31, 2026• 0 citations
Publisher

Abstract

Quantum-Inspired Constraint Satisfaction (QICS) presents a novel approach to constraint satisfaction problems (CSPs) by integrating quantum mechanics principles, specifically the concept of quantum tunneling, into the algorithm's search strategy. The core mechanism involves introducing a subtle 'quantum fluctuation' process that dynamically alters the search path, effectively encouraging the algorithm to escape local optima and explore the solution space more effectively. This research investigates the potential of this technique to surpass traditional CSP methods in terms of solution quality and efficiency. This paper demonstrates the effectiveness of this approach through theoretical analysis and preliminary experimentation, showcasing a significant improvement in performance compared to existing methods.

DOI
10.5281/zenodo.22201180

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Constraint satisfaction problems (CSPs) are ubiquitous in various fields, including artificial intelligence, scheduling, and optimization. Traditional CSP solvers often struggle with large and complex problem instances, leading to significant computational costs. This paper explores the application of quantum-inspired algorithms to address these challenges. We propose a novel approach leveraging concepts from quantum mechanics, specifically superposition and entanglement, to enhance the search process within CSPs. The core idea involves employing techniques such as quantum walks to explore the solution space more efficiently than classical methods. Our framework demonstrates the potential for significant performance improvements in solving CSPs by effectively mimicking quantum computational paradigms within a classical setting. We focus on the theoretical aspects and provide a detailed explanation of the methodology, highlighting its advantages and potential applications. The key contribution lies in the strategic integration of quantum-inspired concepts to optimize the exploration of the solution space of CSPs, offering a promising direction for tackling computationally intensive problems.
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