14 citations · 32 across the 14 of their papers we have counts for
16 papers
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…
The Watermark Shortcut: How Provenance Marking Sabotages Audio Deepfake Detection
Nicolas M. Müller, Pascal Debus
Provenance watermarking is increasingly treated as a safeguard for synthetic speech, whether built directly into speech-generation models such as Chatterbox, provided through dedic…
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…
Old Rules in a New Game: Mapping Uncertainty Quantification to Quantum Machine Learning
Maximilian Wendlinger, Kilian Tscharke, Pascal Debus
One of the key obstacles in traditional deep learning is the reduction in model transparency caused by increasingly intricate model functions, which can lead to problems such as ov…
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…
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 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…
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling
Michael Kölle, Afrae Ahouzi, Pascal Debus +4
Quantum one-class support vector machines leverage the advantage of quantum kernel methods for semi-supervised anomaly detection. However, their quadratic time complexity with resp…
QUACK: Quantum Aligned Centroid Kernel
Kilian Tscharke, Sebastian Issel, Pascal Debus
Quantum computing (QC) seems to show potential for application in machine learning (ML). In particular quantum kernel methods (QKM) exhibit promising properties for use in supervis…
A Comparative Analysis of Adversarial Robustness for Quantum and Classical Machine Learning Models
Maximilian Wendlinger, Kilian Tscharke, Pascal Debus
Quantum machine learning (QML) continues to be an area of tremendous interest from research and industry. While QML models have been shown to be vulnerable to adversarial attacks m…
Towards Classical Software Verification using Quantum Computers
Sebastian Issel, Kilian Tscharke, Pascal Debus
We explore the possibility of accelerating the formal verification of classical programs with a quantum computer. A common source of security flaws stems from the existence of comm…
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements
Michael Kölle, Afrae Ahouzi, Pascal Debus +3
Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision. Utilizing the one-class…
Protecting Publicly Available Data With Machine Learning Shortcuts
Nicolas M. Müller, Maximilian Burgert, Pascal Debus +3
Machine-learning (ML) shortcuts or spurious correlations are artifacts in datasets that lead to very good training and test performance but severely limit the model's generalizatio…
Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel
Kilian Tscharke, Sebastian Issel, Pascal Debus
Anomaly detection (AD) involves identifying observations or events that deviate in some way from the rest of the data. Machine learning techniques have shown success in automating…
Deep Reinforcement Learning for Backup Strategies against Adversaries
Pascal Debus, Nicolas Müller, Konstantin Böttinger
Many defensive measures in cyber security are still dominated by heuristics, catalogs of standard procedures, and best practices. Considering the case of data backup strategies, we…