Quantum-Enhanced Constraint Satisfaction with Dynamic Relaxation

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum annealing, a quantum computing paradigm, offers potential for accelerating computationally intensive problems like constraint satisfaction. This paper explores the integration of quantum annealing with dynamic relaxation to tackle complex constraint satisfaction problems (CSPs) with high-dimensional constraints. We propose a novel hybrid approach where quantum annealing is used to efficiently explore the solution space, followed by dynamic relaxation to refine the solution and improve its quality. The core mechanism centers around leveraging the quantum annealing's ability to find near-optimal solutions, coupled with the dynamic relaxation's capability to iteratively improve the solution's precision. This research aims to address the limitations of traditional CSP solvers and provides a promising avenue for enhancing performance in challenging scenarios.

DOI: 10.5281/zenodo.22265843

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