基于量子纠缠的机器学习模型优化

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum entanglement to enhance the optimization of machine learning models. We propose a novel framework that transforms the traditional parameter and architecture search process into a quantum algorithm, aiming to achieve significantly improved efficiency and performance. The core concept centers around utilizing quantum entanglement to create a parallel and correlated optimization process, potentially bypassing the limitations of classical optimization techniques. This paper presents a detailed theoretical analysis of the proposed mechanism and explores preliminary experimental results demonstrating the potential benefits of this approach. We conclude by outlining future research directions and highlighting the challenges and opportunities associated with translating this quantum optimization concept into practical applications.

DOI: 10.5281/zenodo.22278957

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