Quantum Error Correction Coding and Neural Network Joint Training

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper explores a novel approach to neural network training by synergistically combining quantum error correction (QEC) coding with the training process itself. The core claim is that leveraging the redundancy inherent in QEC schemes, alongside the training dynamics of neural networks, can significantly enhance the robustness and generalization capabilities of neural networks operating in noisy environments. The proposed mechanism involves encoding neural network weights into quantum bits (qubits) utilizing a tailored QEC strategy. During training, feedback from the neural network dynamically adjusts this encoding strategy, improving the network's resilience to noise. We present a theoretical framework outlining this approach, focusing on the key mathematical formulations and demonstrating the potential for improved performance compared to conventional training methods. This work represents a significant step towards creating more efficient and reliable neural network training paradigms.

DOI: 10.5281/zenodo.22157979

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