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@ARTICLE{Pasetto:909757,
      author       = {Pasetto, Edoardo and Riedel, Morris and Melgani, Farid and
                      Michielsen, Kristel and Cavallaro, Gabriele},
      title        = {{Q}uantum {SVR} for {C}hlorophyll {C}oncentration
                      {E}stimation in {W}ater {W}ith {R}emote {S}ensing},
      journal      = {IEEE geoscience and remote sensing letters},
      volume       = {19},
      issn         = {1545-598X},
      address      = {New York, NY},
      publisher    = {IEEE},
      reportid     = {FZJ-2022-03388},
      pages        = {1 - 5},
      year         = {2022},
      abstract     = {The increasing availability of quantum computers motivates
                      researching their potential capabilities in enhancing the
                      performance of data analysis algorithms. Similarly, as in
                      other research communities, also in remote sensing (RS), it
                      is not yet defined how its applications can benefit from the
                      usage of quantum computing (QC). This letter proposes a
                      formulation of the support vector regression (SVR) algorithm
                      that can be executed by D-Wave quantum computers.
                      Specifically, the SVR is mapped to a quadratic unconstrained
                      binary optimization (QUBO) problem that is solved with
                      quantum annealing (QA). The algorithm is tested on two
                      different types of computing environments offered by D-Wave:
                      the advantage system, which directly embeds the problem into
                      the quantum processing unit (QPU), and a hybrid solver that
                      employs both classical and QC resources. For the evaluation,
                      we considered a biophysical variable estimation problem with
                      RS data. The experimental results show that the proposed
                      quantum SVR implementation can achieve comparable or, in
                      some cases, better results than the classical
                      implementation. This work is one of the first attempts to
                      provide insight into how QA could be exploited and
                      integrated in future RS workflows based on machine learning
                      (ML) algorithms.},
      cin          = {JSC},
      ddc          = {550},
      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)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000849255500003},
      doi          = {10.1109/LGRS.2022.3200325},
      url          = {https://juser.fz-juelich.de/record/909757},
}