% 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”.

@ARTICLE{Dobrynin:1037899,
      author       = {Dobrynin, Dmitri and Renaudineau, Adrien and Hizzani,
                      Mohammad and Strukov, Dmitri and Mohseni, Masoud and
                      Strachan, John Paul},
      title        = {{E}nergy landscapes of combinatorial optimization in
                      {I}sing machines},
      journal      = {Physical review / E},
      volume       = {110},
      number       = {4},
      issn         = {2470-0045},
      reportid     = {FZJ-2025-01037},
      pages        = {045308},
      year         = {2024},
      abstract     = {Physics-based Ising machines (IM) have been developed as
                      dedicated processors for solving hard combinatorial
                      optimization problems with higher speed and better energy
                      efficiency. Generally, such systems employ local search
                      heuristics to traverse energy landscapes in searching for
                      optimal solutions. Here, we quantify and address some of the
                      major challenges met by IMs by extending energy-landscape
                      geometry visualization tools known as disconnectivity
                      graphs. Using efficient sampling methods, we visually
                      capture landscapes of problems having diverse structure and
                      hardness manifesting as energetic and entropic barriers for
                      IMs. We investigate energy barriers, local minima, and
                      configuration space clustering effects caused by locality
                      reduction methods when embedding combinatorial problems to
                      the Ising hardware. To this end, we sample disconnectivity
                      graphs of PUBO energy landscapes and their different QUBO
                      mappings accounting for both local minima and saddle
                      regions. We demonstrate that QUBO energy-landscape
                      properties lead to the subpar performance for quadratic IMs
                      and suggest directions for their improvement.},
      cin          = {PGI-14},
      ddc          = {530},
      cid          = {I:(DE-Juel1)PGI-14-20210412},
      pnm          = {5234 - Emerging NC Architectures (POF4-523) / 5232 -
                      Computational Principles (POF4-523)},
      pid          = {G:(DE-HGF)POF4-5234 / G:(DE-HGF)POF4-5232},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {39562922},
      UT           = {WOS:001340811600004},
      doi          = {10.1103/PhysRevE.110.045308},
      url          = {https://juser.fz-juelich.de/record/1037899},
}