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@ARTICLE{Schulz:1047391,
      author       = {Schulz, Sebastian and Willsch, Dennis and Michielsen,
                      Kristel},
      title        = {{L}earning-{D}riven {A}nnealing with {A}daptive
                      {H}amiltonian {M}odification for {S}olving {L}arge-{S}cale
                      {P}roblems on {Q}uantum {D}evices},
      journal      = {Quantum},
      volume       = {9},
      issn         = {2521-327X},
      address      = {Wien},
      publisher    = {Verein zur Förderung des Open Access Publizierens in den
                      Quantenwissenschaften},
      reportid     = {FZJ-2025-04277},
      pages        = {1898},
      year         = {2025},
      abstract     = {We present Learning-Driven Annealing (LDA), a framework
                      that links individual quantum annealing evolutions into a
                      global solution strategy to mitigate hardware constraints
                      such as short annealing times and integrated control errors.
                      Unlike other iterative methods, LDA does not tune the
                      annealing procedure (e.g. annealing time or annealing
                      schedule), but instead learns about the problem structure to
                      adaptively modify the problem Hamiltonian. By deforming the
                      instantaneous energy spectrum, LDA suppresses transitions
                      into high-energy states and focuses the evolution into
                      low-energy regions of the Hilbert space. We demonstrate the
                      efficacy of LDA by developing a hybrid quantum-classical
                      solver for large-scale spin glasses. The hybrid solver is
                      based on a comprehensive study of the internal structure of
                      spin glasses, outperforming other quantum and classical
                      algorithms (e.g., reverse annealing, cyclic annealing,
                      simulated annealing, Gurobi, Toshiba's SBM, VeloxQ and
                      D-Wave hybrid) on 5580-qubit problem instances in both
                      runtime and lowest energy. LDA is a step towards practical
                      quantum computation that enables today's quantum devices to
                      compete with classical solvers.},
      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},
      doi          = {10.22331/q-2025-10-29-1898},
      url          = {https://juser.fz-juelich.de/record/1047391},
}