Quantum Normalizing Flows for Anomaly Detection
This work addresses anomaly detection for quantum computing applications, but it is incremental as it adapts existing normalizing flow concepts to quantum architectures.
The authors tackled anomaly detection by introducing quantum normalizing flows, which map arbitrary distributions to predefined ones using quantum architectures, and achieved competitive performance compared to classical methods like isolation forests and local outlier factor.
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.