Detecting quantum hacking attacks for continuous-variable quantum key distribution using quantum neural network

Junhao Li, Yiyu Mao, Qin Liao, Yan Ding, Zhuo Tang and Kenli Li

Chaos, Solitons & Fractals, 2026, vol. 202, issue P1

Abstract: Continuous-variable quantum key distribution (CVQKD) enables unconditionally secure communications between two distant legitimate users. However, the practical security of CVQKD system is susceptible to various quantum hacking attacks. In this paper, we propose a universal attack defense strategy based on a quantum neural network (QNN), which we call QNN-based attack detection scheme (QADS). Specifically, the classical data collected from a CVQKD system is first encoded into the quadratures of quantum states, which are subsequently used as input quantum data to train the QNN model. Then, the parameters of the QNN are optimized to efficiently learn the underlying characteristics of different types of data, enabling binary classification between normal and attacked signals. Simulation experiments show that the proposed QADS can achieve nearly 100% attack detection accuracy with only a small sacrifice in the raw key, effectively improving the secret key rate of the attack-detectable CVQKD system. Compared with existing classical attack detection schemes, the QADS demonstrates higher convergence efficiency and enhanced detection accuracy. Moreover, the secret key rate underestimated by existing attack-detectable CVQKD system can be completely rectified by the QADS, thereby obtaining an accurate security bound for practical CVQKD system.

Keywords: Quantum neural network; Continuous-variable quantum key distribution; Attack detection (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:202:y:2026:i:p1:s0960077925014808

DOI: 10.1016/j.chaos.2025.117467

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