Quantum Entanglement Machine Learning Algorithm

Jincheng Zhang

Published Sep 1, 2026• 0 citations

Abstract

This paper investigates the potential of quantum entanglement to enhance machine learning algorithms. The core claim posits the design of a novel machine learning algorithm predicated on leveraging quantum entanglement to significantly improve model performance. We explore the theoretical underpinnings of this approach, outlining the key mechanisms and potential benefits. This work presents a framework for utilizing entanglement to potentially overcome limitations inherent in traditional machine learning models, focusing primarily on enhancing data representation and feature extraction. We acknowledge the significant challenges associated with implementing this concept and present preliminary results demonstrating the feasibility of this approach.

DOI: 10.5281/zenodo.22221687

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