  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.
