Deep-learning-based continuous attacks on quantum key distribution protocols

The most important characteristic of a Quantum Key Distribution (QKD) protocol is its security against third-party attacks, and the potential countermeasures available. While new types of attacks are regularly developed in the literature, they rarely involve the use of weak continuous measurement and more specifically machine learning to infer the qubit states. In this paper, we design a new individual attack scheme called \textit{Deep-learning-based continuous attack} (DLCA) that exploits continuous measurement together with the powerful pattern recognition capacities of deep recurrent neural networks. We show that, when applied to the BB84 protocol, our attack increases only slightly the Quantum Bit Error Rate (QBER) of a noisy channel and allows the spy to infer a significant part of the sifted key. In addition, we show it yields similar performances in terms of information gain when compared to an optimal individual attack, namely the phase-covariant quantum cloner. Our individual attack scheme demonstrates deep-learning-enhanced quantum state tomography applied to QKD and could be generalized in many different ways,