Quantum normalizing flows for anomaly detection

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, especially those ones where there are already quantum inspired algorithms available. In the experiments we compare our performace to isolation forests (IF), the local outlier factor (LOF), or single-class SVMs.