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

TL;DR

This paper introduces a novel deep learning-based attack method on quantum key distribution protocols, leveraging continuous measurement and neural networks to infer qubit states, revealing potential vulnerabilities in QKD security.

Contribution

It presents the first use of deep recurrent neural networks for continuous measurement-based attacks on QKD, demonstrating a new class of quantum hacking strategies.

Findings

The attack's performance exceeds standard intercept-resend methods.

The attack approaches the effectiveness of the phase-covariant quantum cloner.

Deep learning enhances quantum state tomography in QKD security analysis.

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 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. As a minimal model, we present its performances when applied in the case of the BB84 protocol with intrinsic noise in the communication channel. Our results suggest that our attack's performances lie between the ones of standard intercept-and-resend attacks and of the optimal individual attack, namely the phase-covariant quantum cloner. Our attack scheme demonstrates deep-learning-enhanced quantum state tomography applied to QKD, and could be generalized in many different ways, notably in the cases of quantum hacking attacks targeting implementation vulnerabilities that could compromise the security of QKD protocols.