119 citations · 152 across the 29 of their papers we have counts for
6 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…
Weight Re-Mapping for Variational Quantum Algorithms
Michael Kölle, Alessandro Giovagnoli, Jonas Stein +5
Inspired by the remarkable success of artificial neural networks across a broad spectrum of AI tasks, variational quantum circuits (VQCs) have recently seen an upsurge in quantum m…
Introducing Reduced-Width QNNs, an AI-inspired Ansatz Design Pattern
Jonas Stein, Tobias Rohe, Francesco Nappi +5
Variational Quantum Algorithms are one of the most promising candidates to yield the first industrially relevant quantum advantage. Being capable of arbitrary function approximatio…
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…
Evidence that PUBO outperforms QUBO when solving continuous optimization problems with the QAOA
Jonas Stein, Farbod Chamanian, Maximilian Zorn +4
Quantum computing provides powerful algorithmic tools that have been shown to outperform established classical solvers in specific optimization tasks. A core step in solving optimi…