Quantum-Enhanced Constraint Satisfaction for Complex Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum-enhanced constraint satisfaction problems (CSP) pose a significant challenge in areas like network design, resource allocation, and optimization. Classical approaches often struggle with the complexity and scalability of these problems, particularly when dealing with large, interconnected networks. This paper introduces a novel quantum algorithm designed to address these limitations by leveraging quantum superposition and entanglement to dramatically accelerate the solution process. We propose a quantum search algorithm that effectively explores the solution space, enabling efficient traversal of the network and the identification of optimal configurations. The core mechanism centers around exploiting quantum properties to overcome the computational bottlenecks inherent in traditional search techniques. This research demonstrates the potential of quantum computation to significantly improve the performance of CSP solvers, offering a promising step towards solving complex network problems more effectively.

DOI: 10.5281/zenodo.22195010

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