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@ARTICLE{Ghosh:1025709,
      author       = {Ghosh, Kumar J. B. and Ghosh, Sumit},
      title        = {{C}lassical and quantum machine learning applications in
                      spintronics},
      journal      = {Digital discovery},
      volume       = {2},
      number       = {2},
      issn         = {2635-098X},
      address      = {Washington DC},
      publisher    = {Royal Society of Chemistry},
      reportid     = {FZJ-2024-03092},
      pages        = {512 - 519},
      year         = {2023},
      abstract     = {In this article we demonstrate the applications of
                      classical and quantum machine learning in quantum transport
                      and spintronics. With the help of a two-terminal device with
                      magnetic impurities we show how machine learning algorithms
                      can predict the highly non-linear nature of conductance as
                      well as the non-equilibrium spin response function for any
                      random magnetic configuration. By mapping this quantum
                      mechanical problem onto a classification problem, we are
                      able to obtain much higher accuracy beyond the linear
                      response regime compared to the prediction obtained with
                      conventional regression methods. We finally describe the
                      applicability of quantum machine learning which has the
                      capability to handle a significantly large configuration
                      space. Our approach is applicable for solid state devices as
                      well as for molecular systems. These outcomes are crucial in
                      predicting the behavior of large-scale systems where a
                      quantum mechanical calculation is computationally
                      challenging and therefore would play a crucial role in
                      designing nanodevices.},
      cin          = {PGI-1},
      ddc          = {004},
      cid          = {I:(DE-Juel1)PGI-1-20110106},
      pnm          = {5211 - Topological Matter (POF4-521)},
      pid          = {G:(DE-HGF)POF4-5211},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001101461800001},
      doi          = {10.1039/D2DD00094F},
      url          = {https://juser.fz-juelich.de/record/1025709},
}