Quantum Memory for Neural Networks with Topological Qubits

Jincheng Zhang

Published Sep 3, 2026• 0 citations

Abstract

This paper explores the potential of employing topological qubits as a robust memory solution for neural networks. The primary challenge in utilizing quantum computing for complex tasks, particularly within neural networks, lies in the decoherence of quantum states. Traditional qubits are highly susceptible to environmental noise, leading to rapid state decay and hindering long-term computations. Topological qubits, due to their inherent properties arising from non-Abelian braiding, offer a promising solution by providing a fundamentally different approach to quantum information storage. This work details a proposed architecture where a neural network is constructed utilizing these topologically protected qubits. We investigate the theoretical framework for encoding and processing information using these qubits, focusing on minimizing decoherence effects. The core claim presented here is that topological qubits can significantly reduce decoherence issues, enabling more complex and longer-term computations within neural networks. We outline a potential computational model and discuss key considerations for implementing such a system, including qubit connectivity, error correction strategies, and the scaling of the network. The research introduces a new approach to quantum memory, directly addressing a critical bottleneck in the advancement of quantum neural networks.

DOI: 10.5281/zenodo.22267534

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