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Quantum-Guided Constraint Optimization

Jincheng Zhang

Published Sep 4, 2026• 0 citations
Publisher

Abstract

Quantum-Guided Constraint Optimization (QGCO) is a novel optimization technique leveraging the unique properties of quantum mechanics to enhance the efficiency of constraint optimization problems. This paper explores the fundamental principles behind QGCO, detailing its core mechanism involving quantum superposition and entanglement to guide the search process, ultimately leading to improved solution quality and reduced computational cost. We present a comprehensive theoretical framework, illustrating the algorithm's functionality through mathematical formulations and illustrative examples. The research focuses on the impact of quantum measurements on the search space, demonstrating a significant improvement over classical optimization methods in several benchmark scenarios. The paper concludes with a discussion of the potential applications of QGCO across diverse fields, including finance, logistics, and materials science.

DOI
10.5281/zenodo.22290491

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