A Hybrid Quantum–Classical Neural Network Framework for the Detection of Quantum Hacking Attacks in CVQKD

The security of continuous-variable quantum key distribution (CVQKD) systems faces severe challenges from quantum hacking attacks in practical deployments. This paper proposes a novel hybrid quantum-classical neural network (HQCNN) architecture for the detection of quantum hacking attacks. This architecture employs a convolutional neural network (CNN) to extract features from raw pulse signals at the receiver and to reduce spatial dimensionality. Subsequently, the extracted features are mapped into a high-dimensional Hilbert space via angle encoding, and a variational quantum circuit (VQC) is utilized as the core classifier for discrimination. In five-class classification experiments involving local oscillator intensity attacks (LOIA), calibration attacks, saturation attacks, hybrid attacks, and the no-attack state, the HQCNN achieves an overall accuracy of 93%, representing a 6% improvement over the classical residual network (ResNet). In addition, the proposed HQCNN architecture exhibits a significant advantage in parameter efficiency compared with classical deep neural networks. This study provides an efficient intelligent detection scheme for enhancing the practical security of CVQKD systems.