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@MASTERSTHESIS{Hanussek:1030222,
      author       = {Hanussek, Philipp Jan},
      title        = {{C}omparison of {F}actoring {A}lgorithms on the {D}-{W}ave
                      {Q}uantum {A}nnealer},
      school       = {FH Aachen},
      type         = {Bachelorarbeit},
      reportid     = {FZJ-2024-05254},
      pages        = {46 pages},
      year         = {2024},
      note         = {Bachelorarbeit, FH Aachen, 2024},
      abstract     = {The goal of this work is to implement and assess different
                      approaches for solving the factoring problem on quantum
                      annealers. We identify three promising approaches that use
                      custom and heuristic embedding and experimentally test their
                      performance on the Advantage quantum annealer by D-Wave
                      Systems Inc. To reduce terms of higher order than quadratic,
                      we formulate an approach that takes into account the
                      coefficient of the term to be reduced, and we show
                      experimentally that it produces valid models for smaller
                      problem sizes. We evaluate the impact of using individual
                      per-qubit offsets and find that this feature can
                      significantly improve the success frequencies for some
                      problem sizes. For others, applying offsets can lead to a
                      decrease in success frequencies.We find that all three
                      examined factoring approaches exhibit a scaling with problem
                      size that is qualitatively similar to random drawing.
                      Generally, all methods fail to find solutions for larger
                      problem sizes. On average, the success frequencies are only
                      $10-100$ times higher than randomly drawing each bit of $p$
                      and $q$. However, the approach with custom embedding is able
                      to find ground states even for larger problem sizes,
                      indicating a problem formulation that is well suited for the
                      quantum annealer.},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)2},
      doi          = {10.34734/FZJ-2024-05254},
      url          = {https://juser.fz-juelich.de/record/1030222},
}