Quantum Entanglement Modeling and Optimization

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

Quantum entanglement, a peculiar phenomenon in quantum mechanics, presents a fascinating opportunity for novel approaches to optimization. This paper explores the potential of utilizing entanglement to construct a non-local optimization algorithm. We propose a model that leverages entanglement to achieve a global optimization strategy, avoiding the pitfalls of local search. The core mechanism focuses on designing an algorithm that exploits instantaneous correlations between entangled particles to guide the search process. We argue that this approach holds significant promise for tackling complex optimization problems, particularly those involving intricate constraints and global optima. The paper concludes with a discussion of the theoretical foundations and potential applications of this method.

DOI: 10.5281/zenodo.22220045

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