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@ARTICLE{Ghosh:1025711,
      author       = {Ghosh, Kumar J. B. and Ghosh, Sumit},
      title        = {{E}xploring exotic configurations with anomalous features
                      with deep learning: {A}pplication of classical and
                      quantum-classical hybrid anomaly detection},
      journal      = {Physical review / B},
      volume       = {108},
      number       = {16},
      issn         = {2469-9950},
      address      = {Woodbury, NY},
      publisher    = {Inst.},
      reportid     = {FZJ-2024-03094},
      pages        = {165408},
      year         = {2023},
      abstract     = {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.},
      cin          = {PGI-1},
      ddc          = {530},
      cid          = {I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521) / DFG project
                      268565370 - TRR 173: Spin+X: Der Spin in seiner kollektiven
                      Umgebung (268565370)},
      pid          = {G:(DE-HGF)POF4-5211 / G:(GEPRIS)268565370},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1103/PhysRevB.108.165408},
      url          = {https://juser.fz-juelich.de/record/1025711},
}