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@ARTICLE{Willsch:866292,
      author       = {Willsch, D. and Willsch, M. and De Raedt, Hans and
                      Michielsen, K.},
      title        = {{S}upport vector machines on the {D}-{W}ave quantum
                      annealer},
      journal      = {Computer physics communications},
      volume       = {248},
      issn         = {0010-4655},
      address      = {Amsterdam},
      publisher    = {North Holland Publ. Co.},
      reportid     = {FZJ-2019-05451},
      pages        = {107006 -},
      year         = {2020},
      abstract     = {Kernel-based support vector machines (SVMs) are supervised
                      machine learning algorithms for classification and
                      regression problems. We introduce a method to train SVMs on
                      a D-Wave 2000Q quantum annealer and study its performance in
                      comparison to SVMs trained on conventional computers. The
                      method is applied to both synthetic data and real data
                      obtained from biology experiments. We find that the quantum
                      annealer produces an ensemble of different solutions that
                      often generalizes better to unseen data than the single
                      global minimum of an SVM trained on a conventional computer,
                      especially in cases where only limited training data is
                      available. For cases with more training data than currently
                      fits on the quantum annealer, we show that a combination of
                      classifiers for subsets of the data almost always produces
                      stronger joint classifiers than the conventional SVM for the
                      same parameters.},
      cin          = {JSC},
      ddc          = {530},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel1)PHD-NO-GRANT-20170405},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000509613900006},
      doi          = {10.1016/j.cpc.2019.107006},
      url          = {https://juser.fz-juelich.de/record/866292},
}