Distributed Quantum Machine Learning Fault-Tolerant Error Correction Codes

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper presents a novel approach to fault-tolerant error correction coding specifically designed for distributed quantum machine learning (DQLM) environments. The core challenge in DQLM lies in preserving the delicate quantum states – superposition and entanglement – necessary for quantum algorithms, while simultaneously mitigating the detrimental effects of noise and decoherence inherent in distributed quantum computation. Existing quantum error correction techniques often require significant overhead and complex control, making them less suitable for the dynamic and resource-constrained nature of DQLM. We introduce a coding scheme predicated on leveraging quantum entanglement and parallel processing capabilities of distributed quantum computers. This scheme provides real-time error correction and recovery, enhancing the accuracy and reliability of machine learning algorithms. The key contribution is a tailored error correction strategy that directly addresses the unique vulnerabilities of DQLM, offering a pathway towards practical and robust quantum machine learning. We formalize the error correction process with mathematical notation, outlining the encoding, decoding, and correction operations. The proposed framework demonstrates significant potential for improving the performance and scalability of DQLM.

DOI: 10.5281/zenodo.22315660

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