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@INPROCEEDINGS{Pazem:916698,
      author       = {Pazem, Josephine and Ansari, Mohammad},
      title        = {{I}mproving the resilience of quantum denoising process},
      reportid     = {FZJ-2023-00038},
      year         = {2022},
      abstract     = {Quantum autoencoders aim to automate denoising algorithms.
                      These quantum neural networks are trained to surpass noise
                      channels and return arbitrary entangled states of our
                      interest with high-fidelity. So far the successful training
                      has shown tolerance up to $30\%$ of bit flip and
                      depolarization. Stronger noise results in poor training and
                      denoising failure. [1]In this talk I describe an inexpensive
                      change in the network topology that can be extendable to all
                      scales and can improve the tolerance significantly. This has
                      a side advantage that it can provide even higher fidelity
                      values for successful training. It indeed helps the encoder
                      by reducing the dimension of the decision boundary between
                      perfect and noisy states. Such a simplification of the
                      classification task relies heavily on quantum properties of
                      the neural units. We show that Renyi entropy associated with
                      a small partition of the network undergoes a second order
                      phase transition when training fails, and this can serve as
                      a good measure to distinguish between failure and success in
                      denoising process. [1] D. Bondarenko and P. Feldmann,
                      “Quantum autoencoders to denoise quantum data”, Phys.
                      Rev. Lett., vol. 124, no. 13, p. 130502, 2020.},
      month         = {Mar},
      date          = {2022-03-14},
      organization  = {APS Meeting 2022, Chicago (USA), 14
                       Mar 2022 - 18 Mar 2022},
      subtyp        = {Invited},
      cin          = {PGI-2},
      cid          = {I:(DE-Juel1)PGI-2-20110106},
      pnm          = {5224 - Quantum Networking (POF4-522)},
      pid          = {G:(DE-HGF)POF4-5224},
      typ          = {PUB:(DE-HGF)6},
      url          = {https://juser.fz-juelich.de/record/916698},
}