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@MASTERSTHESIS{Pazem:941218,
      author       = {Pazem, Josephine},
      title        = {{D}enoising with {Q}uantum {M}achine {L}earning},
      volume       = {82},
      school       = {RWTH Aachen University},
      type         = {Masterarbeit},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2023-00822},
      isbn         = {978-3-95806-641-0},
      series       = {Schriften des Forschungszentrums Jülich Reihe Information
                      / Information},
      pages        = {106},
      year         = {2022},
      note         = {Masterarbeit, RWTH Aachen University, 2022},
      abstract     = {This master thesis explores aspects of quantum machine
                      learning in the light of an application to dampen the
                      effects of noise on NISQ processors. We investigate the
                      possibility of designing machine learning models that can be
                      accommodated entirely on quantum devices without the help of
                      classical computers. With Dissipative Quantum Neural
                      Networks, we simulate a quantum feed-forward neural network
                      for denoising: the Quantum Autoencoder. We assign it to
                      correct bit-flip noise in states that can exist only quantum
                      mechanically, namely the highly entangled GHZ-states. The
                      numerical simulations report that the QAE can recover the
                      target states up to some tolerance threshold on the noise
                      intensity. To understand the limitations, we investigate the
                      mechanisms behind the completion of the denoising task with
                      quantum entropy measures. The observationsreveal that the
                      latent representation is key to reconstructing the desired
                      state in the outputs. Consequently, we propose an
                      inexpensive modification of the original QAE: the brain
                      box-enhanced QAE. The addition of complexity in the
                      intermediate layers of the network maximizes the robustness
                      of the QAE in a setting where only a finite-size training
                      data set is available. We close the argument with a
                      discussion on the generalization properties of the network.},
      cin          = {PGI-2 / IAS-3},
      cid          = {I:(DE-Juel1)PGI-2-20110106 / I:(DE-Juel1)IAS-3-20090406},
      pnm          = {5224 - Quantum Networking (POF4-522)},
      pid          = {G:(DE-HGF)POF4-5224},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)19},
      urn          = {urn:nbn:de:0001-2023013111},
      url          = {https://juser.fz-juelich.de/record/941218},
}