Quantum Error Correction Underpins Distributed Machine Learning Optimization

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

This paper explores the potential of leveraging quantum error correction (QEC) codes to enhance distributed machine learning (DML) optimization. The core claim is that QEC's inherent noise mitigation capabilities can significantly improve the robustness and efficiency of DML model training. The proposed mechanism involves encoding machine learning model parameters into quantum states, utilizing QEC for inter-node communication and computation, and concurrently performing error detection and correction. This represents a novel approach, as QEC and DML have historically been pursued independently. The research investigates the theoretical underpinnings of this integration, focusing on key aspects such as code selection, communication protocols, and error correction strategies within the distributed environment. We examine the impact of varying noise levels and communication constraints on the overall performance of the DML system. The ultimate goal is to demonstrate a framework where QEC's capabilities can be directly translated into a more reliable and scalable DML solution.

DOI: 10.5281/zenodo.22186194

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