Algorithmic Quantum Constraint Optimization

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

Quantum constraint optimization is a burgeoning field aiming to leverage quantum mechanics principles to solve complex optimization problems. This paper presents a novel algorithmic framework, "Quantum Constraint Optimization," designed to address challenges inherent in classical optimization, particularly those involving high dimensionality and intricate constraints. We propose an integration of a quantum annealing framework with a meticulously crafted constraint propagation system, utilizing quantum entanglement to enhance solution discovery. This approach promises superior performance compared to existing methods, particularly for problems exhibiting significant complexity. The core mechanism revolves around optimizing a cost function subject to constraints derived from quantum mechanics, fostering a pathway towards more efficient and robust optimization techniques. This work investigates the potential of this framework to significantly advance the capabilities of quantum optimization, offering a fundamentally different approach to problem-solving.

DOI: 10.5281/zenodo.22263605

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