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@ARTICLE{Hartmann:1051618,
      author       = {Hartmann, Carsten and Zhang, Junjie and Calaza, Carlos D.
                      Gonzalez and Pesch, Thiemo and Michielsen, Kristel and
                      Benigni, Andrea},
      title        = {{Q}uantum {A}nnealing {B}ased {P}ower {G}rid {P}artitioning
                      for {P}arallel {S}imulation},
      journal      = {IEEE transactions on power systems},
      volume       = {40},
      number       = {6},
      issn         = {0885-8950},
      address      = {New York, NY, USA},
      publisher    = {IEEE},
      reportid     = {FZJ-2026-00540},
      pages        = {4958 - 4970},
      year         = {2025},
      abstract     = {Graph partitioning has many applications in power systems,
                      from decentralized state estimation to parallel simulation.
                      Focusing on parallel simulation, optimal grid partitioning
                      minimizes the idle time caused by different simulation times
                      for the sub-networks and their components and reduces the
                      overhead required to simulate the cuts. Partitioning a graph
                      into two parts such that, for example, the cut is minimal
                      and the sub-graphs have equal size is an NP-hard problem. In
                      this paper, we show how optimal partitioning of a graph can
                      be obtained using quantum annealing (QA). We show how to map
                      the requirements for optimal splitting to a quadratic
                      unconstrained binary optimization (QUBO) formulation and
                      test the proposed formulation using a current D-Wave QPU. We
                      show that the necessity to find an embedding of the QUBO on
                      current D-Wave QPUs limits the problem size to under 200
                      buses and notably affects the time-to-solution. We finally
                      discuss the implications of quantum hardware non-ideality on
                      near term implementation in the simulation loop.},
      cin          = {JSC / ICE-1},
      ddc          = {620},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)ICE-1-20170217},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / QuGrids -
                      Quantum-based Energy Grids (QuGrids20231101)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(MKW-NRW)QuGrids20231101},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.1109/TPWRS.2025.3578243},
      url          = {https://juser.fz-juelich.de/record/1051618},
}