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@ARTICLE{Demidik:643430,
      author       = {Demidik, Maria and Tüysüz, Cenk and Piatkowski, Nico and
                      Grossi, Michele and Jansen, Karl},
      title        = {{E}xpressive equivalence of classical and quantum
                      restricted {B}oltzmann machines},
      reportid     = {PUBDB-2026-00191, arXiv:2502.17562},
      year         = {2025},
      note         = {11 pages, 4 figures; supplementary material 6 pages, 1
                      figure},
      abstract     = {The development of generative models for quantum machine
                      learning has faced challenges such as trainability and
                      scalability. A notable example is the quantum restricted
                      Boltzmann machine (QRBM), where non-commuting Hamiltonians
                      make gradient evaluation computationally demanding, even on
                      fault-tolerant devices. In this work, we propose a
                      semi-quantum restricted Boltzmann machine (sqRBM), a model
                      designed to overcome difficulties associated with QRBMs. The
                      sqRBM Hamiltonian commutes in the visible subspace while
                      remaining non-commuting in the hidden subspace, enabling us
                      to derive closed-form expressions for output probabilities
                      and gradients. Our analysis shows that, for learning a given
                      distribution, a classical model requires three times more
                      hidden units than an sqRBM. Numerical simulations with up to
                      100 units validate this prediction. With reduced resource
                      demands, sqRBMs provide a feasible framework for early
                      quantum generative models.},
      cin          = {CQTA},
      ddc          = {530},
      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)25},
      eprint       = {2502.17562},
      howpublished = {arXiv:2502.17562},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2502.17562;\%\%$},
      url          = {https://bib-pubdb1.desy.de/record/643430},
}