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Paper 1

Fault Injection Attacks on Machine Learning-based Quantum Computer Readout Error Correction

Anthony Etim, Jakub Szefer

Year

2025

Journal

arXiv preprint

DOI

arXiv:2512.20077

arXiv

2512.20077

Machine-learning (ML) classifiers are increasingly used in quantum computing systems to improve multi-qubit readout discrimination and to mitigate correlated readout errors. These ML classifiers are an integral component of today's quantum computer's control and readout stacks. This paper is the first to analyze the susceptibility of such ML classifiers to physical fault-injection which can result in generation of incorrect readout results from quantum computers. The study targets 5-qubit (thus 32-class) readout error-correction model. Using the ChipWhisperer Husky for physical voltage glitching, this work leverages an automated algorithm for scanning the fault injection parameter search space to find various successful faults in all the layers of the target ML model. Across repeated trials, this work finds that fault susceptibility is strongly layer-dependent: early-layers demonstrate higher rates of misprediction when faults are triggered in them, whereas later layers have smaller misprediction rates. This work further characterizes the resulting readout failures at the bitstring level using Hamming-distance and per-bit flip statistics, showing that single-shot glitches can induce structured readout corruption rather than purely random noise. These results motivate treating ML-based quantum computer readout and readout correction as a security-critical component of quantum systems and highlight the need for lightweight, deployment-friendly fault detection and redundancy mechanisms in the quantum computer readout pipelines.

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Paper 2

Entanglement-assisted Quantum Error Correcting Code Saturating The Classical Singleton Bound

Soham Ghosh, Evagoras Stylianou, Holger Boche

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.04130

arXiv

2410.04130

We introduce a construction for entanglement-assisted quantum error-correcting codes (EAQECCs) that saturates the classical Singleton bound with less shared entanglement than any known method for code rates below $ \frac{k}{n} = \frac{1}{3} $. For higher rates, our EAQECC also meets the Singleton bound, although with increased entanglement requirements. Additionally, we demonstrate that any classical $[n,k,d]_q$ code can be transformed into an EAQECC with parameters $[[n,k,d;2k]]_q$ using $2k$ pre-shared maximally entangled pairs. The complexity of our encoding protocol for $k$-qudits with $q$ levels is $\mathcal{O}(k \log_{\frac{q}{q-1}}(k))$, excluding the complexity of encoding and decoding the classical MDS code. While this complexity remains linear in $k$ for systems of reasonable size, it increases significantly for larger-levelled systems, highlighting the need for further research into complexity reduction.

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