Quantum Entanglement for Probabilistic Machine Learning

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre phenomenon where two or more particles become linked in such a way that they share the same fate, has captivated scientists and philosophers alike. This paper explores the potential of quantum entanglement as a foundation for a novel probabilistic machine learning framework. We propose a method that leverages quantum entanglement to enhance the randomness and precision of the generation process, addressing limitations inherent in traditional machine learning algorithms. The core claim is that this framework will yield significantly improved results, particularly in scenarios demanding high accuracy and robustness. This work demonstrates the practical application of quantum entanglement in a probabilistic machine learning context, outlining a mechanism to achieve this enhanced randomness. The paper concludes with a discussion of the challenges and future research directions.

DOI: 10.5281/zenodo.22638240

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