Distributed Machine Learning Based on Multivariate Entanglement

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores the application of quantum entanglement in distributed machine learning (DML) to address the challenges of data sharing and model merging across multiple nodes. Traditional DML systems often suffer from significant communication overhead and computational bottlenecks due to the need for centralized data storage and model aggregation. We propose a novel framework leveraging multivariate entanglement to facilitate efficient data sharing and model consolidation. The core concept involves entangling the input data streams of multiple learning nodes. This entanglement allows for correlated data access and enables a distributed model merging process that minimizes communication costs and computational complexity. This approach offers the potential to significantly improve the accuracy and efficiency of DML systems compared to conventional methods. The theoretical framework is presented, outlining the key equations and mathematical models employed to demonstrate the feasibility and performance of the proposed approach. We define key terms and concepts, focusing on the entanglement entropy, correlation coefficients, and the overall performance metrics used to evaluate the effectiveness of the distributed learning system. The paper concludes with a discussion of future research directions and potential applications of this technology.

DOI: 10.5281/zenodo.22152707

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