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Detecting quantum hacking attacks for continuous-variable quantum key distribution using quantum neural network
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DOI：10.1016/j.chaos.2025.117467.png)
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Abstract
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• Establish a QNN model to learn key features from CVQKD data for attack detection. • QADS achieves perfect attack detection with only minimal raw key consumption. • QADS outperforms classical methods in accuracy and convergence. • QADS rectifies underestimated secret key rate in attack-detectable CVQKD systems.
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