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@ARTICLE{Locher:1005289,
      author       = {Locher, David and Cardarelli, Lorenzo and Müller, Markus},
      title        = {{Q}uantum {E}rror {C}orrection with {Q}uantum
                      {A}utoencoders},
      journal      = {Quantum},
      volume       = {7},
      issn         = {2521-327X},
      address      = {Wien},
      publisher    = {Verein zur Förderung des Open Access Publizierens in den
                      Quantenwissenschaften},
      reportid     = {FZJ-2023-01404},
      pages        = {942 -},
      year         = {2023},
      abstract     = {Active quantum error correction is a central ingredient to
                      achieve robust quantum processors. Inthis paper we
                      investigate the potential of quantum machine learning for
                      quantum error correction.Specifically, we demonstrate how
                      quantum neural networks, in the form of quantum
                      autoencoders,can be trained to learn optimal strategies for
                      active detection and correction of errors,
                      includingspatially correlated computational errors as well
                      as qubit losses. We highlight that the denoisingcapabilities
                      of quantum autoencoders are not limited to the protection of
                      specific states but extendto the entire logical codespace.
                      We also show that quantum neural networks can be used to
                      discovernew logical encodings that are optimally adapted to
                      the underlying noise. Moreover, we find that,even in the
                      presence of moderate noise in the quantum autoencoders
                      themselves, they may still besuccessfully used to perform
                      beneficial quantum error correction.},
      cin          = {PGI-2},
      ddc          = {530},
      cid          = {I:(DE-Juel1)PGI-2-20110106},
      pnm          = {5221 - Advanced Solid-State Qubits and Qubit Systems
                      (POF4-522)},
      pid          = {G:(DE-HGF)POF4-5221},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000979644900001},
      doi          = {10.22331/q-2023-03-09-942},
      url          = {https://juser.fz-juelich.de/record/1005289},
}