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This research demonstrates how reinforcement learning enables adaptive eavesdropping attacks on quantum key distribution systems, achieving 98% of the theoretical information limit by exploiting channel fluctuations over time. The findings reveal that current security assessments for QKD networks may be overly conservative because they fail to account for these learned, dynamic attack strategies. Consequently, this work highlights a critical gap in the safety protocols for metropolitan and government quantum communication infrastructures.
Researchers Boost Eavesdropping Success to 0.348 with Learnt Attacks
Under bilateral depolarising noise, a reinforcement learning attacker now achieves 98% of the theoretical limit for eavesdropping on device-independent E91 protocols, raising Holevo information from 0. 135 to 0. 348.
An attacker employing reinforcement learning can gain information about a quantum key distribution system by adapting their strategy to changes in signal transmission over time, with this adaptive behaviour previously unquantified. The modelling as a sequential decision process enables attacks to be learnt instead of pre-defined, extending its application to more complex channel conditions including those lacking known templates. An attacker can exploit fluctuations within quantum communication channels to gain more information than previously considered…
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