Quantum Normalizing Flows for Anomaly Detection

Autor:	Rosenhahn, Bodo, Hirche, Christoph
Rok vydání:	2024
Předmět:	Quantum Physics Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	A Normalizing Flow computes a bijective mapping from an arbitrary distribution to a predefined (e.g. normal) distribution. Such a flow can be used to address different tasks, e.g. anomaly detection, once such a mapping has been learned. In this work we introduce Normalizing Flows for Quantum architectures, describe how to model and optimize such a flow and evaluate our method on example datasets. Our proposed models show competitive performance for anomaly detection compared to classical methods, esp. those ones where there are already quantum inspired algorithms available. In the experiments we compare our performance to isolation forests (IF), the local outlier factor (LOF) or single-class SVMs. Comment: v3: 15 pages, 8 figures
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2402.02866 Zobrazit plný text záznamu View this record from Arxiv