Machine learning (ML) decoders are increasingly used in quantum error correction (QEC) to model noise, analyze imperfect syndrome measurements, and replace iterative matching algorithms with a learned inference pass, making them a critical component of the classical control stack of a fault-tolerant quantum computer. Prior security analysis of the ML decoders in the controller stack has addressed integrity and fault-injection attacks on the decoders. This work is the first to explore physical power side-channel attacks on ML decoders. In particular, it studies a convolutional Deep Q-Learning decoder for the distance-5 surface code. This work profiles the decoder to capture the power signatures generated during classification tasks and trains a classifier to identify which qubit the decoder identified as having errors based on the power signatures. The trained classifier is then applied to test data, where it recovers the correct data qubit label from the decoder’s side-channel data with up to
83.2% accuracy across 25 classes, against
4% for random guessing. A second classifier is trained to identify the type of error labeled by the decoder, Pauli X or Z, and reaches
84.6% accuracy, against
50% for random guessing. This work further performs an initial study of a timing channel, which complements the power side channels, exploring how the duration of the decoder’s first convolutional layer can leak the code size for which the decoder is compiled. All the side-channel attacks demonstrated in this work motivate treating the embedded QEC decoder as a confidentiality-critical component of the quantum control stack.
Quantum communication is a key enabler of next-generation networks, leveraging quantum entanglement to enable a new class of information exchange. While prior work has focused on the theoretical analysis of communication protocols, their exposure to physical layer side channel analysis remains largely unexplored. In classical systems, side channel analysis has been shown to reveal sensitive information without accessing the underlying data, raising the question of whether similar risks exist in quantum networks. In this work, we investigate whether different quantum communication protocols exhibit distinguishable signatures that can be inferred through passive side channel observations. We consider a threat model in which an observer accesses only a fraction of the optical signal without directly measuring the encoded quantum states. Under this setting, we experimentally examine four representative protocols, namely entanglement distribution, quantum gate sequences, heralded quantum key distribution, and quantum identity authentication, realized on a polarization entangled photon link. Observable physical layer features, including single photon detection statistics and optical power measurements, are collected and used to construct protocol fingerprints. We develop a data-driven framework for protocol identification based on these observations. Our results show that protocol identity can be inferred with accuracy reaching up to 96% under 30:70 sampling configuration/optical tapping, while remaining distinguishable at 10:90 with accuracy ranging from 70-89%. Bell inequality measurements confirm that the sampling/tapping process preserves entanglement, validating the non-destructive nature of the observation model. These findings demonstrate that side channel analysis can expose protocol-level information without disrupting quantum correlations, introducing new security considerations.
True random number generators (TRNGs) are significant piece of hardware security that are used to generate secret keys, initial values or random masks for counter measures against side-channel attacks. Thus the security of implementation in hardware of such block is crucial. The work presented in this paper show that it is possible to find information on a TRNG using its electromagnetic radiations. The proposed analysis is suitable to retrieve information on the localization, oscillator frequency and sampling frequency of the TRNG.