Quantum Entanglement-Based Distributed Compression Learning

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores a novel approach to distributed compression learning leveraging the unique properties of quantum entanglement. The core idea is to establish a distributed learning system where nodes collaborate to compress data without requiring direct data transmission, thereby potentially achieving lower latency and increased efficiency compared to traditional methods. We introduce a framework where data is partitioned and each node processes a segment, utilizing entangled states for efficient information sharing and collaborative learning. The inherent noise introduced by entanglement decoherence is not treated as an obstacle, but rather as a source of information that is actively exploited through a specialized learning algorithm. The system's performance is evaluated through theoretical analysis and conceptual design, demonstrating the potential benefits of this entanglement-based approach. Key aspects covered include the mathematical formulation of the entanglement-based learning process, the noise modeling strategies, and the optimization techniques employed. The proposed system's architecture and learning algorithm provide a foundation for future research into quantum-enhanced distributed learning.

DOI: 10.5281/zenodo.22274475

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