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@ARTICLE{Willsch:910749,
      author       = {Willsch, Dennis and Willsch, Madita and Jin, Fengping and
                      Michielsen, Kristel and De Raedt, Hans},
      title        = {{GPU}-accelerated simulations of quantum annealing and the
                      quantum approximate optimization algorithm},
      journal      = {Computer physics communications},
      volume       = {278},
      issn         = {0010-4655},
      address      = {Amsterdam},
      publisher    = {North Holland Publ. Co.},
      reportid     = {FZJ-2022-04119},
      pages        = {108411},
      year         = {2022},
      abstract     = {We study large-scale applications using a GPU-accelerated
                      version of the massively parallel Jülich universal quantum
                      computer simulator (JUQCS–G). First, we benchmark JUWELS
                      Booster, a GPU cluster with 3744 NVIDIA A100 Tensor Core
                      GPUs. Then, we use JUQCS–G to study the relation between
                      quantum annealing (QA) and the quantum approximate
                      optimization algorithm (QAOA). We find that a very coarsely
                      discretized version of QA, termed approximate quantum
                      annealing (AQA), performs surprisingly well in comparison to
                      the QAOA. It can either be used to initialize the QAOA, or
                      to avoid the costly optimization procedure altogether.
                      Furthermore, we study the scaling of the success probability
                      when using AQA for problems with 30 to 40 qubits. We find
                      that the case with the largest discretization error scales
                      most favorably, surpassing the best result obtained from the
                      QAOA.},
      cin          = {JSC},
      ddc          = {530},
      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)16},
      UT           = {WOS:000831314600011},
      doi          = {10.1016/j.cpc.2022.108411},
      url          = {https://juser.fz-juelich.de/record/910749},
}