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@ARTICLE{Delilbasic:1018738,
      author       = {Delilbasic, Amer and Le Saux, Bertrand and Riedel, Morris
                      and Michielsen, Kristel and Cavallaro, Gabriele},
      title        = {{A} {S}ingle-{S}tep {M}ulticlass {SVM} {B}ased on {Q}uantum
                      {A}nnealing for {R}emote {S}ensing {D}ata {C}lassification},
      journal      = {IEEE journal of selected topics in applied earth
                      observations and remote sensing},
      volume       = {17},
      issn         = {1939-1404},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-05019},
      pages        = {1434 - 1445},
      year         = {2024},
      abstract     = {In recent years, the development of quantum annealers has
                      enabled experimental demonstrations and has increased
                      research interest in applications of quantum annealing, such
                      as in quantum machine learning and in particular for the
                      popular quantum Support Vector Machine (SVM). Several
                      versions of the quantum SVM have been proposed, and quantum
                      annealing has been shown to be effective in them. Extensions
                      to multiclass problems have also been made, which consist of
                      an ensemble of multiple binary classifiers. This work
                      proposes a novel quantum SVM formulation for direct
                      multiclass classification based on quantum annealing, called
                      Quantum Multiclass SVM (QMSVM). The multiclass
                      classification problem is formulated as a single quadratic
                      unconstrained binary optimization problem solved with
                      quantum annealing. The main objective of this work is to
                      evaluate the feasibility, accuracy, and time performance of
                      this approach. Experiments have been performed on the D-Wave
                      Advantage quantum annealer for a classification problem on
                      remote sensing data. Results indicate that, despite the
                      memory demands of the quantum annealer, QMSVM can achieve an
                      accuracy that is comparable to standard SVM methods, such as
                      the one-versus-one (OVO), depending on the dataset (compared
                      to OVO: 0.8663 vs 0.8598 on Toulouse, 0.8123 vs 0.8521 on
                      Potsdam). More importantly, it scales much more efficiently
                      with the number of training examples, resulting in nearly
                      constant time (compared to OVO: 85.72s vs 248.02s on
                      Toulouse, 58.89s vs 580.17s on Potsdam). This work shows an
                      approach for bringing together classical and quantum
                      computation, solving practical problems in remote sensing
                      with current hardware.},
      cin          = {JSC},
      ddc          = {520},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511) / RAISE - Research on AI- and Simulation-Based
                      Engineering at Exascale (951733) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(EU-Grant)951733 / G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001127459900006},
      doi          = {10.1109/JSTARS.2023.3336926},
      url          = {https://juser.fz-juelich.de/record/1018738},
}