Quantum Error Correction for Classical Neural Networks

Jincheng Zhang

Published Aug 31, 2026• 0 citations

Abstract

Classical neural networks, despite their remarkable success in various domains, are inherently vulnerable to noise and errors, stemming from factors such as numerical instability, stochastic gradient descent, and hardware limitations. This paper proposes a novel approach to enhance the resilience of neural networks by integrating quantum error correction (QEC) directly into their architecture. The core idea is to design and implement a QEC code specifically adapted to the unique error characteristics of classical neural networks. This is achieved by constructing layers within the network that actively monitor and correct for these errors. The theoretical framework outlines the principles of this approach, detailing the mathematical formulation of the error correction process and the key design considerations. We demonstrate, through a detailed analysis, that this method offers a significant improvement in the robustness of neural networks compared to conventional approaches, particularly in scenarios with high levels of noise. The presented framework paves the way for a new generation of robust and reliable neural networks capable of operating in challenging environments.

DOI: 10.5281/zenodo.22195564

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