<ul><li>Quantum Neural Networks (QNNs) have shown significant value across domains, with well-trained QNNs representing critical intellectual property often deployed via cloud-based QNN-as-a-Service (QNNaaS) platforms.</li><li>Existing approaches for QNN model extraction attacks have largely overlooked the impact of varying quantum noise inherent in noisy intermediate-scale quantum (NISQ) computers, limiting their effectiveness in real-world settings.</li><li>The CopyQNN framework proposes a three-step data cleaning method to eliminate noisy data based on its noise sensitivity, followed by the integration of contrastive and transfer learning within the quantum domain.</li><li>Experimental results on NISQ computers demonstrate that the practical implementation of CopyQNN outperforms state-of-the-art QNN extraction attacks, achieving an average performance improvement of 8.73% while reducing the number of required queries by 90x.</li></ul>

CopyQNN: Quantum Neural Network Extraction Attack under Varying Quantum Noise

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