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

• URL: http://arxiv.org/abs/2408.12571v2
• Date: Fri, 4 Oct 2024 16:57:10 GMT
• Title: Deep-learning-based continuous attacks on quantum key distribution protocols
• Authors: Théo Lejeune, François Damanet,
• Abstract summary: We design a new attack scheme 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 can be difficult to notice while still allowing the spy to extract significant information about the states of the qubits sent in the quantum communication channel.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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. Here, we design a new 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 can be difficult to notice while still allowing the spy to extract significant information about the states of the qubits sent in the quantum communication channel. Finally, we study how the spy can exploit quantum feedback to further cover their tracks. Our attack scheme, while still at the early stages of a toy model, constitutes a potential threat which is worthwhile to be investigated, also as it could be applied to different QKD protocols and generalized in many different ways.

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