  In this article 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 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.
