13 citations · 33 across the 13 of their papers we have counts for
4 papers · 2 filters
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…
Towards Transfer Learning for Large-Scale Image Classification Using Annealing-based Quantum Boltzmann Machines
Daniëlle Schuman, Leo Sünkel, Philipp Altmann +4
Quantum Transfer Learning (QTL) recently gained popularity as a hybrid quantum-classical approach for image classification tasks by efficiently combining the feature extraction cap…
Benchmarking Quantum Surrogate Models on Scarce and Noisy Data
Jonas Stein, Michael Poppel, Philip Adamczyk +9
Surrogate models are ubiquitously used in industry and academia to efficiently approximate given black box functions. As state-of-the-art methods from classical machine learning fr…
Exploring Unsupervised Anomaly Detection with Quantum Boltzmann Machines in Fraud Detection
Jonas Stein, Daniëlle Schuman, Magdalena Benkard +7
Anomaly detection in Endpoint Detection and Response (EDR) is a critical task in cybersecurity programs of large companies. With rapidly growing amounts of data and the omnipresenc…