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@ARTICLE{Pasetto:1020574,
      author       = {Pasetto, Edoardo and Riedel, Morris and Michielsen, Kristel
                      and Cavallaro, Gabriele},
      title        = {{K}ernel {A}pproximation on a {Q}uantum {A}nnealer for
                      {R}emote {S}ensing {R}egression {T}asks},
      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-2024-00269},
      pages        = {3262 - 3269},
      year         = {2024},
      abstract     = {The increased development of quantum computing hardware in
                      recent years has led to increased interest in its
                      application to various areas. Finding effective ways to
                      apply this technology to real-world use-cases is a current
                      area of research in the (RS) community. This paper proposes
                      an (AQKS) kernel approximation algorithm with parallel
                      quantum annealing on the D-Wave Advantage quantum annealer.
                      The proposed implementation is applied to (SVR) and (GPR)
                      algorithms. To evaluate its performance, a regression
                      problem related to estimating chlorophyll concentration in
                      water is considered. The proposed algorithm was tested on
                      two real-world datasets and its results were compared with
                      those obtained by a classical implementation of kernel-based
                      algorithms and a (RKS) implementation. On average, the
                      parallel (AQKS) achieved comparable results to the benchmark
                      methods, indicating its potential for future applications.},
      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) / EUROCC-2 (DEA02266) / RAISE - Research on AI-
                      and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(DE-Juel-1)DEA02266 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:001166899900001},
      doi          = {10.1109/JSTARS.2024.3350385},
      url          = {https://juser.fz-juelich.de/record/1020574},
}