基于量子纠缠的机器学习模型

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper introduces a novel machine learning model based on quantum entanglement, aiming to enhance model parameter optimization and improve the performance of machine learning algorithms. We propose a mechanism utilizing inherent quantum entanglement in the model parameters to dynamically adjust them, offering a departure from traditional, fixed parameter adjustment techniques. The model's effectiveness is demonstrated through theoretical analysis and preliminary experimentation, showcasing a potential for significantly improved learning rates and generalization capabilities. This work explores the potential of quantum phenomena to revolutionize machine learning model design and training.

DOI: 10.5281/zenodo.22330318

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