Based on Quantum Entanglement: Adaptive Machine Learning

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper investigates the potential of quantum entanglement to enhance machine learning algorithms, specifically through the development of an adaptive learning framework. The core concept revolves around leveraging quantum entanglement to improve data representation and learning efficiency, ultimately boosting the model's generalization ability. We present a novel approach utilizing entanglement to dynamically adjust the learning process, offering a potentially transformative advancement in machine learning performance. This research explores the theoretical foundations and practical implementation of this framework, highlighting its advantages over traditional methods. The paper concludes with a discussion of the challenges and future directions for this exciting research area.

DOI: 10.5281/zenodo.22153334

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