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@INPROCEEDINGS{Pasetto:1017951,
      author       = {Pasetto, Edoardo and Riedel, Morris and Michielsen, Kristel
                      and Cavallaro, Gabriele},
      title        = {{A}diabatic {Q}uantum {K}itchen {S}inks with {P}arallel
                      {A}nnealing for {R}emote {S}ensing {R}egression {P}roblems},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-04456},
      pages        = {784-787},
      year         = {2023},
      abstract     = {Kernel methods are class of Machine Learning (ML) models
                      that have been widely employed in the literature for Earth
                      Observation (EO) applications. The increasing development of
                      quantum computing hardware motivates further research to
                      improve the capabilities and the performances of data
                      analysis algorithms. In this manuscript an implementation of
                      Adiabatic Quantum Kitchen Sinks (AQKS) kernel estimation
                      algorithm integrated with parallel quantum annealing is
                      presented. Such combination with the concept of parallel
                      quantum annealing allows for the solving of multiple problem
                      instances in the same annealing cycle, thus reducing the
                      number of rquired calls to the quantum annealing solver. The
                      proposed workflow is then implemented using a D-Wave
                      Advantage system and tested on a regression problem on a
                      real Remote Sensing (RS) dataset. The obtained results are
                      then analyzed and compared with those obtained by a
                      classical kernel approximation algorithm based on Random
                      Fourier Features.},
      month         = {Jul},
      date          = {2023-07-16},
      organization  = {IEEE International Geoscience and
                       Remote Sensing Symposium (IGARSS),
                       Pasadena (CA), 16 Jul 2023 - 21 Jul
                       2023},
      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) / EUROCC-2 (DEA02266)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001098971601022},
      doi          = {10.1109/IGARSS52108.2023.10281523},
      url          = {https://juser.fz-juelich.de/record/1017951},
}