Quantum-Driven Constraint Satisfaction with Adaptive Relaxation

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

Quantum-driven constraint satisfaction is an emerging field that leverages quantum mechanics to solve complex optimization problems, particularly those involving constraint satisfaction. This paper presents a novel approach to this problem, employing a quantum annealing algorithm coupled with an adaptive relaxation scheme. We demonstrate that by dynamically adjusting the relaxation criteria during the quantum annealing process, the algorithm significantly improves solution quality compared to traditional methods. The core mechanism centers around a quantum-enhanced exploration of the solution space, guided by a feedback loop that adapts to the system's state. This adaptive relaxation strategy promises to address key limitations of existing constraint satisfaction techniques, offering the potential for enhanced performance and robustness.

DOI: 10.5281/zenodo.22316153

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