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@ARTICLE{Pasetto:909064,
      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 with {R}emote {S}ensing},
      reportid     = {FZJ-2022-02982},
      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. This paper 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) optimization 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 quantum computing
                      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 algorithms.},
      cin          = {JSC},
      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)25},
      doi          = {10.36227/techrxiv.19619676.v1},
      url          = {https://juser.fz-juelich.de/record/909064},
}