Quantum Error Correcting Neural Networks (QENN)

Jincheng Zhang

Published Aug 29, 2026• 0 citations

Abstract

This paper proposes a novel architecture, the Quantum Error Correcting Neural Network (QENN), which leverages quantum error correction techniques to construct a fault-tolerant neural network. The core concept involves integrating classical neural networks with quantum error correction codes, such as the Quantum Capacity Decoding Error (QCDE), to mitigate the detrimental effects of quantum decoherence on neural network performance. Each neuron within the QENN utilizes a quantum bit (qubit) to represent its state, enabling the application of quantum error correction codes to detect and correct errors introduced by environmental noise. This approach aims to enhance the scalability and resilience of neural networks, potentially surpassing the capabilities of conventional neural networks in complex computational tasks. The paper details the theoretical framework, explores the integration of QCDE, and outlines a potential architecture for QENNs, focusing on the fundamental principles of combining classical and quantum computation for enhanced neural network functionality. The primary goal is to demonstrate the feasibility and advantages of utilizing quantum error correction within a neural network paradigm.

DOI: 10.5281/zenodo.22156899

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