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@ARTICLE{Bdeker:1006815,
      author       = {Bödeker, Lukas and Fiorelli, Eliana and Müller, Markus},
      title        = {{O}ptimal storage capacity of quantum {H}opfield neural
                      networks},
      journal      = {Physical review research},
      volume       = {5},
      number       = {2},
      issn         = {2643-1564},
      address      = {College Park, MD},
      publisher    = {APS},
      reportid     = {FZJ-2023-01868},
      pages        = {023074},
      year         = {2023},
      abstract     = {Quantum neural networks form one pillar of the emergent
                      field of quantum machine learning. Here quantum
                      generalizations of classical networks realizing associative
                      memories—capable of retrieving patterns, or memories, from
                      corrupted initial states—have been proposed. It is a
                      challenging open problem to analyze quantum associative
                      memories with an extensive number of patterns and to
                      determine the maximal number of patterns the quantum
                      networks can reliably store, i.e., their storage capacity.
                      In this work, we propose and explore a general method for
                      evaluating the maximal storage capacity of quantum neural
                      network models. By generalizing what is known as Gardner's
                      approach in the classical realm, we exploit the theory of
                      classical spin glasses for deriving the optimal storage
                      capacity of quantum networks with quenched pattern
                      variables. As an example, we apply our method to an
                      open-system quantum associative memory formed of interacting
                      spin-1/2 particles realizing coupled artificial neurons. The
                      system undergoes a Markovian time evolution resulting from a
                      dissipative retrieval dynamics that competes with a coherent
                      quantum dynamics. We map out the nonequilibrium phase
                      diagram and study the effect of temperature and Hamiltonian
                      dynamics on the storage capacity. Our method opens an avenue
                      for a systematic characterization of the storage capacity of
                      quantum associative memories.},
      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:000985637100010},
      doi          = {10.1103/PhysRevResearch.5.023074},
      url          = {https://juser.fz-juelich.de/record/1006815},
}