Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized Measurements

no code implementations • 14 Dec 2023 • Michael Kölle, Afrae Ahouzi, Pascal Debus, Robert Müller, Danielle Schuman, Claudia Linnhoff-Popien

Quantum computing, with its potential to enhance various machine learning tasks, allows significant advancements in kernel calculation and model precision.

Anomaly Detection

Protecting Publicly Available Data With Machine Learning Shortcuts

no code implementations • 30 Oct 2023 • Nicolas M. Müller, Maximilian Burgert, Pascal Debus, Jennifer Williams, Philip Sperl, Konstantin Böttinger

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 generalization capability.

Semisupervised Anomaly Detection using Support Vector Regression with Quantum Kernel

no code implementations • 1 Aug 2023 • Kilian Tscharke, Sebastian Issel, Pascal Debus

Kernel methods based on quantum kernel estimation have emerged as a promising approach to QML on NISQ devices, offering theoretical guarantees, versatility, and compatibility with NISQ constraints.

Anomaly Detection Quantum Machine Learning +1

Deep Reinforcement Learning for Backup Strategies against Adversaries

no code implementations • 12 Feb 2021 • Pascal Debus, Nicolas Müller, Konstantin Böttinger

In this setting, the usual round-robin scheme, which always replaces the oldest backup, is no longer optimal with respect to avoidable exposure.

reinforcement-learning Reinforcement Learning (RL)