% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Delilbasic:1031806,
      author       = {Delilbasic, Amer and Le Saux, Bertrand and Riedel, Morris
                      and Michielsen, Kristel and Cavallaro, Gabriele},
      title        = {{R}everse {Q}uantum {A}nnealing for {H}ybrid
                      {Q}uantum-{C}lassical {S}atellite {M}ission {P}lanning},
      publisher    = {IEEE},
      reportid     = {FZJ-2024-05826},
      pages        = {432-436},
      year         = {2024},
      abstract     = {The trend of building larger and more complex imaging
                      satellite constellations leads to the challenge of managing
                      multiple acquisition requests for the Earth's surface.
                      Optimally planning these acquisitions is an intractable
                      optimization problem, and heuristic algorithms are used
                      today to find sub-optimal solutions. Recently, quantum
                      algorithms have been considered for this purpose due to the
                      potential breakthroughs they can bring in optimization,
                      expecting either a speedup or an increase in solution
                      quality. Hybrid quantum-classical methods have been
                      considered a short-term solution for taking advantage of
                      small quantum machines. In this paper, we propose reverse
                      quantum annealing as a method for improving the acquisition
                      plan obtained by a classical optimizer. We investigate the
                      benefits of the method with different annealing schedules
                      and different problem sizes. The obtained results provide
                      guidelines for designing a larger hybrid quantum-classical
                      framework based on reverse quantum annealing for this
                      application.},
      month         = {Jul},
      date          = {2024-07-07},
      organization  = {IGARSS 2024 - 2024 IEEE International
                       Geoscience and Remote Sensing
                       Symposium, Athens (Greece), 7 Jul 2024
                       - 12 Jul 2024},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) 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:(EU-Grant)951733 /
                      G:(DE-Juel-1)DEA02266},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:001316158500100},
      doi          = {10.1109/IGARSS53475.2024.10640974},
      url          = {https://juser.fz-juelich.de/record/1031806},
}