14 citations · 32 across the 14 of their papers we have counts for
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quant-ph2023★ 1 cited
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…
quant-ph2023★ 14 cited
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…