Quantum Entanglement-Based Machine Learning Model

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Quantum entanglement, a bizarre quantum phenomenon, holds the potential to revolutionize various fields, including machine learning. This paper explores the use of quantum entanglement to accelerate machine learning model training and inference. We propose a novel machine learning model architecture that leverages the unique properties of entangled states to significantly enhance these processes. The core mechanism involves exploiting entanglement to expedite the exploration of the solution space, leading to faster convergence and improved accuracy. The research details the theoretical underpinnings of this approach, presents a preliminary implementation, and discusses potential future advancements.

DOI: 10.5281/zenodo.22197818

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