Exploring exotic configurations with anomalous features with deep learning: Application of classical and quantum-classical hybrid anomaly detection

In this paper we present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configuration with anomalous features. We consider the Anderson model as a prototype where we define two types of anomalies - a high conductance in presence of strong impurity and low conductance in presence of weak impurity - as a function of random impurity distribution. Such anomalous outcome constitutes less than 10% of a data set and is not a part of the training process. The anomaly detection is therefore more suitable to detect unknown features which is not possible with conventional classification or regression methods. We also present a systematic study of the performance of the classical and the hybrid method and show that the inclusion of a quantum circuit significantly enhances the performance of anomaly detection which we quantify with suitable performance metrics. Our approach is quite generic in nature and can be used for any system that relies on a large number of parameters to find their new configurations which can hold exotic new features.