Quantum-Based Constraint Relaxation for Optimization Problems

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum-based constraint relaxation is a novel approach to solving optimization problems, offering the potential for significantly faster and more accurate solutions compared to classical methods. This paper explores the application of quantum annealing and quantum simulation to iteratively refine constraints, thereby accelerating the optimization process. We detail the underlying principles of these quantum algorithms, present a framework for implementing them, and discuss the potential benefits and challenges of this approach. The core claim is that quantum-based constraint relaxation can dramatically improve the performance of optimization algorithms.

DOI: 10.5281/zenodo.22635527

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