Quantum Neural Network Architectures Based on Topological Qubits

Jincheng Zhang

Published Sep 5, 2026• 0 citations

Abstract

This paper explores the design and implementation of quantum neural network (QNN) architectures leveraging the unique properties of topological qubits. Traditional QNNs face significant challenges regarding decoherence and scalability. Utilizing the inherent robustness of topological qubits – specifically their protection against local perturbations – presents a novel approach to constructing more reliable and efficient QNNs. We propose a framework for building QNNs based on topologically protected quantum bits, outlining the core mechanisms for information processing and demonstrating the potential for enhanced training stability. The architecture emphasizes the use of braiding operations to induce entanglement and the subsequent application of quantum algorithms for learning and inference. This work addresses a critical need for robust quantum computing platforms suitable for complex neural network training, moving beyond the limitations of current approaches. The presented architecture offers a pathway towards scalable and fault-tolerant QNNs, representing a significant step in realizing the full potential of quantum computation for artificial intelligence.

DOI: 10.5281/zenodo.22338551

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