Quantum Neural Network Extraction Attack via Split Co-Teaching

Zhenxiao Fu; Fan Chen

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arXiv · 2409.02207

Quantum Neural Network Extraction Attack via Split Co-Teaching

Zhenxiao Fu·Fan Chen

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Abstract

Quantum Neural Networks (QNNs), now offered as QNN-as-a-Service (QNNaaS), have become key targets for model extraction attacks. Existing methods use ensemble learning to train substitute QNNs, but our analysis reveals significant limitations in real-world environments, where noise and cost constraints undermine their effectiveness. In this work, we introduce a novel attack, \textit{split co-teaching}, which uses label variations to \textit{split} queried data by noise sensitivity and employs \textit{co-teaching} schemes to enhance extraction accuracy. The experimental results show that our approach outperforms classical extraction attacks by 6.5\%$\sim$9.5\% and existing QNN extraction methods by 0.1\%$\sim$3.7\% across various tasks.

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Zhenxiao Fu, Fan Chen. 2024-09-03. Quantum Neural Network Extraction Attack via Split Co-Teaching. https://arxiv.org/abs/2409.02207

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