基于量子纠缠的机器学习模型训练

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel machine learning model training approach utilizing quantum entanglement. We propose a method that leverages the inherent dynamic properties of quantum entanglement to automate the training process, allowing for more efficient and stable model optimization. The resulting model demonstrates enhanced accuracy and stability compared to traditional machine learning algorithms. This work addresses a critical challenge in machine learning: the often-limited capacity of classical models to effectively explore complex parameter spaces. The core mechanism centers around the application of quantum entanglement to guide the optimization process, offering a fundamentally different approach to learning.

DOI: 10.5281/zenodo.22330474

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