Quantum Error Correction Driven Distributed Machine Learning

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores a novel approach to distributed machine learning (DML) that leverages the inherent error correction capabilities of quantum error correction (QEC) codes. The core claim is that incorporating QEC can significantly enhance the robustness of DML systems, particularly in environments with noisy communication channels. The proposed mechanism involves designing a QEC code specifically tailored to protect the parameter updates within a distributed model. The feedback signals generated by the QEC code are then utilized to intelligently adjust the model parameters, mitigating the detrimental effects of noise. This contrasts with conventional DML methods that often struggle to cope with communication noise, offering a direct improvement in system resilience through the utilization of QEC's properties. The theoretical framework presented demonstrates the potential for achieving high accuracy even under challenging conditions, marking a significant advancement in the intersection of quantum computing and machine learning. The key aspects explored include code selection, decoding strategies, and the integration of the decoded updates into the distributed model. The paper culminates with a discussion of potential challenges and future research directions.

DOI: 10.5281/zenodo.22191357

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