Quantum-Enhanced Constraint Solving with Adaptive Relaxation

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

Quantum-enhanced constraint solving aims to accelerate the process of finding solutions to optimization problems by leveraging the unique capabilities of quantum computation. This paper introduces a novel approach that employs quantum annealing or variational quantum eigensolver (VQE) to dynamically adjust relaxation parameters during the constraint solving process, thereby minimizing computational cost and improving solution quality. We demonstrate the effectiveness of this method through a series of simulations and analysis, showcasing significant gains in solving complex constraint satisfaction problems. The core mechanism involves strategically utilizing quantum algorithms to explore the solution space more efficiently.

DOI: 10.5281/zenodo.22303683

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