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：归一化流通过计算从任意分布到预定义分布（如正态分布）的双射映射来实现数据转换。一旦学习到这种映射关系，该流模型可用于解决异常检测等不同任务。本研究针对量子计算架构提出了量子归一化流方法，详细阐述了此类流模型的建模与优化过程，并在示例数据集上进行了评估。实验表明，相较于经典方法（特别是已有量子启发的算法），我们提出的模型在异常检测任务中表现出具有竞争力的性能。在实验中，我们将所提方法与孤立森林、局部离群因子以及单类别支持向量机进行了性能对比。