Quantum Error Correction and Neuromorphic Architectures for Distributed Computing

Jincheng Zhang

Published Sep 2, 2026• 0 citations

Abstract

This paper proposes a novel computational architecture that synergistically combines quantum error correction techniques with neuromorphic systems inspired by biological neural networks. The core claim is to design an architecture capable of efficiently correcting quantum errors while simultaneously providing high-parallelism and fault-tolerance for distributed computing. This is achieved by integrating Quantum Error Correcting Codes (QECCs), specifically Low-Density Parity-Check (LDPC) codes, with neuromorphic networks, such as Spiking Neural Networks (SNNs). The SNNs handle the encoding and decoding of quantum information, leveraging their local connectivity and parallel processing capabilities to accelerate error correction and data transmission. This architecture aims to mitigate the classical computational bottleneck traditionally associated with quantum error correction and to enhance the practicality and efficiency of quantum computing. The key innovation lies in the collaborative operation of these two distinct computational paradigms. This architecture offers a pathway towards scalable and robust quantum computing solutions.

DOI: 10.5281/zenodo.22242437

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