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@INPROCEEDINGS{Nicoli:619663,
      author       = {Nicoli, Kim A. and Anders, Christopher J. and Funcke, Lena
                      and Hartung, Tobias and Jansen, Karl and Kühn, Stefan and
                      Müller, Klaus-Robert and Stornati, Paolo and Kessel, Pan
                      and Nakajima, Shinichi},
      title        = {{P}hysics-{I}nformed {B}ayesian {O}ptimization of
                      {V}ariational {Q}uantum {C}ircuits},
      reportid     = {PUBDB-2024-07802, arXiv:2406.06150},
      pages        = {36},
      year         = {2024},
      note         = {36 pages, 17 figures, 37th Conference on Neural Information
                      Processing Systems (NeurIPS 2023)},
      abstract     = {In this paper, we propose a novel and powerful method to
                      harness Bayesian optimization for Variational Quantum
                      Eigensolvers (VQEs) -- a hybrid quantum-classical protocol
                      used to approximate the ground state of a quantum
                      Hamiltonian. Specifically, we derive a VQE-kernel which
                      incorporates important prior information about quantum
                      circuits: the kernel feature map of the VQE-kernel exactly
                      matches the known functional form of the VQE's objective
                      function and thereby significantly reduces the posterior
                      uncertainty. Moreover, we propose a novel acquisition
                      function for Bayesian optimization called Expected Maximum
                      Improvement over Confident Regions (EMICoRe) which can
                      actively exploit the inductive bias of the VQE-kernel by
                      treating regions with low predictive uncertainty as
                      indirectly ``observed''. As a result, observations at as few
                      as three points in the search domain are sufficient to
                      determine the complete objective function along an entire
                      one-dimensional subspace of the optimization landscape. Our
                      numerical experiments demonstrate that our approach improves
                      over state-of-the-art baselines.},
      month         = {Dec},
      date          = {2023-12-10},
      organization  = {37th Conference on Neural Information
                       Processing Systems, New Orleans (United
                       States), 10 Dec 2023 - 16 Dec 2023},
      keywords     = {optimization: variational (INSPIRE) / dimension: 1
                      (INSPIRE) / quantum circuit: variational (INSPIRE) /
                      variational quantum eigensolver (INSPIRE) / Bayesian
                      (INSPIRE) / Hamiltonian (INSPIRE) / ground state (INSPIRE) /
                      hybrid (INSPIRE) / landscape (INSPIRE)},
      cin          = {CQTA},
      cid          = {I:(DE-H253)CQTA-20221102},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) / QUEST -
                      QUantum computing for Excellence in Science and Technology
                      (101087126)},
      pid          = {G:(DE-HGF)POF4-611 / G:(EU-Grant)101087126},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)8},
      eprint       = {2406.06150},
      howpublished = {arXiv:2406.06150},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2406.06150;\%\%$},
      doi          = {10.3204/PUBDB-2024-07802},
      url          = {https://bib-pubdb1.desy.de/record/619663},
}