基于量子纠缠的非线性自适应机器学习

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper introduces a novel approach to machine learning utilizing quantum entanglement to enhance model sensitivity and improve performance, specifically targeting complex data distributions. We propose a method for dynamically combining multiple machine learning models via quantum entanglement, creating a system that is more robust to data variations and capable of tackling intricate problems. The core mechanism hinges on leveraging the inherent correlations within entangled states to introduce a level of non-linearity into the learning process. This is presented as a significant advancement over current techniques, promising to yield models with higher accuracy and better generalization capabilities, particularly in scenarios involving high-dimensional or non-linear data. We detail the theoretical framework underpinning this approach, explore the implementation details, and provide preliminary results demonstrating the potential of this method. The paper concludes with a discussion of future research directions, emphasizing the significance of quantum entanglement for machine learning.

DOI: 10.5281/zenodo.22578743

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