Quantum-Enhanced Constraint Satisfaction with Adaptive Relaxation

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the application of quantum annealing to optimize constraint satisfaction problems (CSPs). Traditional CSP solvers often struggle with finding optimal solutions, particularly in large and complex instances, necessitating a significant computational cost. We propose a novel quantum algorithm, the Quantum Relaxation Network (QRN), designed to dynamically adjust the relaxation of constraints during the search process. This adaptive approach promises to improve solution quality and reduce computational complexity, offering a significant advancement over existing techniques. We detail the QRN's architecture, the underlying quantum annealing process, and its potential impact on CSP solving. The core claim is that this quantum algorithm will demonstrably enhance solution quality, particularly for challenging CSP instances, offering a viable path toward more efficient and robust CSP solvers.

DOI: 10.5281/zenodo.22190546

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