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@ARTICLE{Bode:1010402,
      author       = {Bode, Tim and Bagrets, Dmitry and Misra-Spieldenner, Aditi
                      and Stollenwerk, Tobias and Wilhelm-Mauch, Frank},
      title        = {{QAOA}.jl: {T}oolkit for the {Q}uantum and {M}ean-{F}ield
                      {A}pproximate {O}ptimization {A}lgorithms},
      journal      = {The journal of open source software},
      volume       = {8},
      number       = {86},
      issn         = {2475-9066},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {[Verlag nicht ermittelbar]},
      reportid     = {FZJ-2023-03042},
      pages        = {5364 -},
      year         = {2023},
      abstract     = {Quantum algorithms are an area of intensive research thanks
                      to their potential for speedingup certain specific tasks
                      exponentially. However, for the time being, high error rates
                      on theexisting hardware realizations preclude the
                      application of many algorithms that are basedon the
                      assumption of fault-tolerant quantum computation. On such
                      noisy intermediate-scale quantum (NISQ) devices (Preskill,
                      2018), the exploration of the potential of heuristicquantum
                      algorithms has attracted much interest. A leading candidate
                      for solving combinatorialoptimization problems is the
                      so-called Quantum Approximate Optimization Algorithm
                      (QAOA)(Farhi et al., 2014).QAOA.jl is a Julia package
                      (Bezanson et al., 2017) that implements the mean-field
                      Ap-proximate Optimization Algorithm (mean-field AOA)
                      (Misra-Spieldenner et al., 2023) - aquantum-inspired
                      classical algorithm derived from the QAOA via the mean-field
                      approximation.This novel algorithm is useful in assisting
                      the search for quantum advantage by providing atool to
                      discriminate (combinatorial) optimization problems that can
                      be solved classically fromthose that cannot. Note that
                      QAOA.jl has already been used during the research leading
                      toMisra-Spieldenner et al. (2023).Additionally, QAOA.jl also
                      implements the QAOA efficiently to support the extensive
                      classicalsimulations typically required in research on the
                      topic. The corresponding parameterizedcircuits are based on
                      Yao.jl (Luo et al., 2020, 2023) and Zygote.jl (Innes et al.,
                      2019, 2023),making it both fast and automatically
                      differentiable, thus enabling gradient-based optimization.A
                      number of common optimization problems such as MaxCut, the
                      minimum vertex-coverproblem, the Sherrington-Kirkpatrick
                      model, and the partition problem are pre-implemented
                      tofacilitate scientific benchmarking.},
      cin          = {PGI-12},
      ddc          = {004},
      cid          = {I:(DE-Juel1)PGI-12-20200716},
      pnm          = {5214 - Quantum State Preparation and Control (POF4-521)},
      pid          = {G:(DE-HGF)POF4-5214},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21105/joss.05364},
      url          = {https://juser.fz-juelich.de/record/1010402},
}