Quantum Error Correction for Distributed Machine Learning Optimization

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper explores the potential of utilizing quantum error correction (QEC) codes to address the challenges of optimization within distributed machine learning (DML) systems. Traditional DML frameworks are susceptible to communication noise, leading to errors in the optimization process and ultimately impacting the quality of the learned models. This work proposes a novel approach where QEC codes are leveraged to correct these errors during information transmission between distributed nodes. The core concept hinges on decomposing the machine learning task across multiple nodes and employing QEC to ensure the accurate propagation of information, thereby enhancing the robustness and efficiency of the optimization process. We present a theoretical framework outlining the implementation and demonstrate the potential for significant improvements in DML performance compared to conventional methods, particularly in environments with high communication noise. The key innovation lies in directly applying the error correction capabilities of QEC to the communication channel, rather than relying on post-processing techniques. This offers a fundamentally different approach with the potential to unlock the benefits of DML in scenarios previously limited by communication constraints.

DOI: 10.5281/zenodo.22249234

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