Quantum Entanglement-Driven Distributed Learning Networks

Jincheng Zhang

Published Sep 7, 2026• 0 citations

Abstract

This paper proposes a novel approach to distributed learning leveraging the unique properties of quantum entanglement. Traditional distributed learning suffers from significant communication bottlenecks, particularly in large-scale and high-dimensional datasets. We introduce a network architecture where nodes are directly connected via entangled quantum states, facilitating instantaneous information transfer and enabling highly efficient collaborative learning. The core mechanism involves utilizing quantum processors within each node to manipulate entangled states for information aggregation and model updates. This approach theoretically bypasses the latency and bandwidth limitations inherent in classical communication, potentially leading to faster convergence rates and improved scalability of distributed learning systems. We explore the fundamental mathematical principles underpinning this system, focusing on the impact of entanglement on information transfer and learning dynamics. The presented framework offers a compelling alternative to existing methods and opens new avenues for research in quantum-enhanced distributed computing.

DOI: 10.5281/zenodo.22635625

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