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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

QKAN: Quantum Kolmogorov-Arnold Networks

Petr Ivashkov, Po-Wei Huang, Kelvin Koor, Lirandë Pira, Patrick Rebentrost

Year

2024

Journal

arXiv preprint

DOI

arXiv:2410.04435

arXiv

2410.04435

The potential of learning models in quantum hardware remains an open question. Yet, the field of quantum machine learning persistently explores how these models can take advantage of quantum implementations. Recently, a new neural network architecture, called Kolmogorov-Arnold Networks (KAN), has emerged, inspired by the compositional structure of the Kolmogorov-Arnold representation theorem. In this work, we design a quantum version of KAN called QKAN. Our QKAN exploits powerful quantum linear algebra tools, including quantum singular value transformation, to apply parameterized activation functions on the edges of the network. QKAN is based on block-encodings, making it inherently suitable for direct quantum input. Furthermore, we analyze its asymptotic complexity, building recursively from a single layer to an end-to-end neural architecture. The gate complexity of QKAN scales linearly with the cost of constructing block-encodings for input and weights, suggesting broad applicability in tasks with high-dimensional input. QKAN serves as a trainable quantum machine learning model by combining parameterized quantum circuits with established quantum subroutines. Lastly, we propose a multivariate state preparation strategy based on the construction of the QKAN architecture.

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