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

We present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configurations with anomalous features. We consider the Anderson model as a prototype, where we define two types of anomalies---a high conductance in the presence of strong impurity and a low conductance in the presence of weak impurity---as a function of random impurity distribution. Such anomalous outcome constitutes an imperceptible fraction of the data set and is not a part of the training process. These exotic configurations, which can be a source of rich new physics, usually remain elusive to conventional classification or regression methods and can be tracked only with a suitable anomaly detection scheme. We also present a systematic study of the performance of the classical and the quantum-classical hybrid anomaly detection 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.