8 papers · 1 filter
A Multiclass Quantum Aligned Centroid Kernel
Kilian Tscharke, Pascal Debus
Kernel methods are powerful tools in machine learning but commonly used full-Gram kernels face three key limitations: (1) quadratic scaling with training set size; (2) the use of f…
An End-to-End Multi-Stage Kill-Chain Attack on Quantum Neural Networks: Demonstration on Trapped-Ion Hardware
Cedric Brügmann, Daniel Herr, Daniel Ohl de Mello +7
We demonstrate an end-to-end, multi-stage attack against a quantum neural network (QNN) model that is executed on a trapped-ion quantum computer. Our chain combines side-channel re…
Quantum Autoencoder for Multivariate Time Series Anomaly Detection
Kilian Tscharke, Maximilian Wendlinger, Afrae Ahouzi +4
Anomaly Detection (AD) defines the task of identifying observations or events that deviate from typical - or normal - patterns, a critical capability in IT security for recognizing…
Quantum Support Vector Regression for Robust Anomaly Detection
Kilian Tscharke, Maximilian Wendlinger, Sebastian Issel +1
Anomaly Detection (AD) is critical in data analysis, particularly within the domain of IT security. In this study, we explore the potential of Quantum Machine Learning for applicat…
Quantum Machine Learning Playground
Pascal Debus, Sebastian Issel, Kilian Tscharke
This article introduces an innovative interactive visualization tool designed to demystify quantum machine learning (QML) algorithms. Our work is inspired by the success of classic…
Entangled Threats: A Unified Kill Chain Model for Quantum Machine Learning Security
Pascal Debus, Maximilian Wendlinger, Kilian Tscharke +7
Quantum Machine Learning (QML) systems inherit vulnerabilities from classical machine learning while introducing new attack surfaces rooted in the physical and algorithmic layers o…