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,