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@ARTICLE{Tueysuez:611180,
      author       = {Tueysuez, Cenk and Chang, Su Yeon and Demidik, Maria and
                      Jansen, Karl and Vallecorsa, Sofia and Grossi, Michele},
      title        = {{S}ymmetry {B}reaking in {G}eometric {Q}uantum {M}achine
                      {L}earning in the {P}resence of {N}oise},
      journal      = {PRX quantum},
      volume       = {5},
      number       = {3},
      issn         = {2691-3399},
      address      = {College Park, MD},
      publisher    = {American Physical Society},
      reportid     = {PUBDB-2024-04833, arXiv:2401.10293},
      pages        = {030314},
      year         = {2024},
      abstract     = {Geometric quantum machine learning based on equivariant
                      quantum neural networks (EQNNs) recently appeared as a
                      promising direction in quantum machine learning. Despite
                      encouraging progress, studies are still limited to theory,
                      and the role of hardware noise in EQNN training has never
                      been explored. This work studies the behavior of EQNN models
                      in the presence of noise. We show that certain EQNN models
                      can preserve equivariance under Pauli channels, while this
                      is not possible under the amplitude damping channel. We
                      claim that the symmetry breaks linearly in the number of
                      layers and noise strength. We support our claims with
                      numerical data from simulations as well as hardware up to 64
                      qubits. Furthermore, we provide strategies to enhance the
                      symmetry protection of EQNN models in the presence of
                      noise.},
      keywords     = {Quantum Physics (quant-ph) (Other) / Machine Learning
                      (cs.LG) (Other) / FOS: Physical sciences (Other) / FOS:
                      Computer and information sciences (Other)},
      cin          = {$Z_ZPPT$},
      ddc          = {530},
      cid          = {$I:(DE-H253)Z_ZPPT-20210408$},
      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)16},
      eprint       = {2401.10293},
      howpublished = {arXiv:2401.10293},
      archivePrefix = {arXiv},
      SLACcitation = {$\%\%CITATION$ = $arXiv:2401.10293;\%\%$},
      UT           = {WOS:001275565000001},
      doi          = {10.1103/PRXQuantum.5.030314},
      url          = {https://bib-pubdb1.desy.de/record/611180},
}