基于量子纠错码的分布式机器学习

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum error-correcting codes to enhance the robustness and scalability of distributed machine learning (DL) models. Traditional DL models are vulnerable to noise and data corruption in distributed environments, posing significant challenges for reliable training and inference. We propose a framework where the model parameters are encoded into quantum error-correcting codes, leveraging the inherent error correction capabilities of these codes to protect against noise. This approach allows for the detection and correction of errors introduced during transmission and computation, ultimately improving the accuracy and reliability of the distributed DL model. The core claim of this work is to demonstrate the feasibility of using quantum error-correcting codes to provide resilience to parameter updates in distributed machine learning. The mechanism involves encoding model parameters into a quantum code, followed by applying quantum error correction algorithms. This represents a novel approach to distributed DL, offering a pathway to more robust and scalable solutions. We will outline the key concepts and theoretical considerations involved in this framework.

DOI: 10.5281/zenodo.22339775

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