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@INPROCEEDINGS{Delilbasic:893824,
      author       = {Delilbasic, Amer and Cavallaro, Gabriele and Willsch,
                      Madita and Melgani, Farid and Riedel, Morris and Michielsen,
                      Kristel},
      title        = {{Q}uantum {S}upport {V}ector {M}achine {A}lgorithms for
                      {R}emote {S}ensing {D}ata {C}lassification},
      publisher    = {IEEE},
      reportid     = {FZJ-2021-02863},
      pages        = {2608-2611},
      year         = {2021},
      comment      = {2021 IEEE International Geoscience and Remote Sensing
                      Symposium IGARSS : [Proceedings] - IEEE, 2021. - ISBN
                      978-1-6654-0369-6 - doi:10.1109/IGARSS47720.2021.9554802},
      booktitle     = {2021 IEEE International Geoscience and
                       Remote Sensing Symposium IGARSS :
                       [Proceedings] - IEEE, 2021. - ISBN
                       978-1-6654-0369-6 -
                       doi:10.1109/IGARSS47720.2021.9554802},
      abstract     = {Recent developments in Quantum Computing (QC) have paved
                      the way for an enhancement of computing capabilities.
                      Quantum Machine Learning (QML) aims at developing Machine
                      Learning (ML) models specifically designed for quantum
                      computers. The availability of the first quantum processors
                      enabled further research, in particular the exploration of
                      possible practical applications of QML algorithms. In this
                      work, quantum formulations of the Support Vector Machine
                      (SVM) are presented. Then, their implementation using
                      existing quantum technologies is discussed and Remote
                      Sensing (RS) image classification is considered for
                      evaluation.},
      month         = {Jul},
      date          = {2021-07-12},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS),
                       Brussels (Belgium), 12 Jul 2021 - 16
                       Jul 2021},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511) / AIDAS - Joint Virtual Laboratory for AI,
                      Data Analytics and Scalable Simulation $(aidas_20200731)$ /
                      AISee - AI- and Simulation-Based Engineering at Exascale
                      (951733)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      $G:(DE-Juel-1)aidas_20200731$ / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      UT           = {WOS:001250139802200},
      doi          = {10.1109/IGARSS47720.2021.9554802},
      url          = {https://juser.fz-juelich.de/record/893824},
}