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.

翻译：归一化流能够计算从任意分布到预定义（如正态分布）的双射映射。一旦学习到这样的映射，该流可用于处理不同任务，例如异常检测。本文首次将归一化流引入量子架构，描述了如何建模与优化此类流，并在示例数据集上评估了该方法。实验表明，与经典方法（尤其是已有量子启发式算法的场景）相比，我们提出的模型在异常检测中展现出具有竞争力的性能。在实验中，我们将性能与孤立森林（IF）、局部异常因子（LOF）和单类支持向量机进行了比较。