Quantum Error Correction Based Topological Protected Quantum Neural Networks

Jincheng Zhang

Published Aug 28, 2026• 0 citations

Abstract

This paper proposes a novel architecture for quantum neural networks (QNNs) that leverages the benefits of topological protection and quantum error correction (QEC). The core idea is to construct a QNN based on a network of non-Abelian anyons, exploiting their inherent robustness against local perturbations. We introduce a framework where topological protection significantly reduces the impact of noise on the network, while LDPC (Low-Density Parity-Check) codes are employed to actively correct errors in the quantum information. The resulting system demonstrates a high tolerance to noise, a critical requirement for scalable QNN implementations. This approach represents a significant advancement in QNN design, merging the strengths of topological quantum computing and established QEC techniques. The theoretical framework outlines the key components and provides a pathway toward realizing fault-tolerant QNNs. The proposed architecture offers a promising route toward building practical and reliable quantum computing systems.

DOI: 10.5281/zenodo.22137697

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