基于动态量子纠缠的自适应机器学习

Jincheng Zhang

Published Sep 4, 2026• 0 citations

Abstract

This paper presents a novel self-adaptive machine learning model leveraging the principles of dynamic quantum entanglement. Traditional machine learning algorithms often struggle with high-dimensional data and complex correlations, limiting their performance. This research introduces a quantum-enhanced approach that exploits the unique properties of quantum entanglement to improve data association and feature extraction. The core mechanism involves utilizing quantum entanglement to dynamically adjust the weighting of data points, leading to enhanced model accuracy and efficiency. We explore the potential of this approach to overcome limitations of classical machine learning in scenarios demanding high-dimensional data and intricate correlations. The study highlights the benefits of employing quantum entanglement within the framework of self-adaptive learning, proposing a new method for improving model performance and robustness.

DOI: 10.5281/zenodo.22303534

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