Quantum-Driven Constraint Relaxation for Complex Systems

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel technique for addressing complex constraint satisfaction problems, leveraging the power of quantum-driven constraint relaxation. The core of this approach is a dynamic learning algorithm that iteratively adjusts relaxation parameters based on observed solutions, aiming to efficiently overcome challenging constraints and improve solution quality. We introduce a framework for dynamically adapting these parameters, enabling a continuous optimization process crucial for problems with intricate relationships and high-dimensional constraints. The concept of quantum-driven relaxation provides a potential pathway for enhanced efficiency and robustness compared to traditional methods. This work presents a theoretical foundation and preliminary implementation demonstrating the efficacy of this approach.

DOI: 10.5281/zenodo.22186946

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