Quantum Machine Learning via Entanglement and Decoherence

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum entanglement and decoherence for distributed machine learning (DML). Traditional DML faces limitations regarding scalability and robustness due to communication overhead and susceptibility to errors. We propose a novel algorithm that utilizes the inherent properties of quantum entanglement to facilitate information sharing between distributed quantum nodes while simultaneously harnessing the effects of decoherence to introduce a degree of error correction. The core idea is to decompose a machine learning task into sub-problems solved independently by each node. Quantum entanglement ensures correlation between node states, and controlled decoherence is employed to dynamically adjust the correlations, mitigating the impact of noise. We present a mathematical framework outlining the algorithm's operation, including the key equations governing the entanglement distribution, decoherence dynamics, and the iterative learning process. This approach offers a promising pathway toward more efficient and resilient DML systems, potentially surpassing the capabilities of classical methods in specific scenarios. The primary contributions of this work lie in the conceptual design and the formalization of the algorithm, laying the groundwork for future experimental investigations.

DOI: 10.5281/zenodo.22136736

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