89 citations
- Technical University of MunichDE15 papers
- University of LimerickIE4 papers
- Ostbayerische Technische Hochschule Amberg-WeidenDE2 papers
- University of SouthamptonGB2 papers
- Worcester Polytechnic InstituteUS2 papers
- Federal Office for Information SecurityDE1 paper
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- Microsoft (Canada)CA1 paper
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- Systems, Applications & Products in Data Processing (Germany)DE1 paper
6 papers · 1 filter
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…
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…
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…
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…
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…