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\%∼9.5\% and existing QNN extraction methods by 0.1\%∼3.7\% across various tasks.
@article{arxiv.2409.02207,
title = {Quantum Neural Network Extraction Attack via Split Co-Teaching},
author = {Zhenxiao Fu and Fan Chen},
journal= {arXiv preprint arXiv:2409.02207},
year = {2025}
}