Quantum Entanglement-Driven Constraint Optimization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement, a phenomenon where two or more particles become linked and share the same fate, presents a novel approach to optimization challenges. This paper investigates the application of entanglement to constraint optimization, proposing a quantum constraint optimization algorithm designed to surpass the limitations of classical techniques. We explore the potential of utilizing entanglement to generate correlated variables, enabling simultaneous exploration of multiple solution spaces with significantly increased computational power. The core mechanism focuses on the creation of entangled states that can be manipulated to efficiently explore the solution space. The theoretical foundation and preliminary experimental results demonstrate the feasibility of this approach, offering a promising pathway toward enhanced optimization performance.

DOI: 10.5281/zenodo.22219254

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