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In this article we present the application of classical and quantum-classicalhybrid anomaly detection schemes to explore exotic configuration with anomalousfeatures. We consider the Anderson model as a prototype where we define twotypes of anomalies - a high conductance in presence of strong impurity and lowconductance in presence of weak impurity - as a function of random impuritydistribution. Such anomalous outcome constitutes an imperceptible fraction ofthe data set and is not a part of the training process. These exoticconfigurations, which can be a source of rich new physics, usually remainelusive to conventional classification or regression methods and can be trackedonly with a suitable anomaly detection scheme. We also present a systematicstudy of the performance of the classical and the quantum-classical hybridanomaly detection method and show that the inclusion of a quantum circuitsignificantly enhances the performance of anomaly detection which we quantifywith suitable performance metrics. Our approach is quite generic in nature andcan be used for any system that relies on a large number of parameters to findtheir new configurations which can hold exotic new features.

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