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@PHDTHESIS{Preti:1047289,
      author       = {Preti, Francesco},
      title        = {{O}ptimal control and machine learning of quantum device
                      dynamics},
      volume       = {302},
      school       = {Köln},
      type         = {Dissertation},
      address      = {Jülich},
      publisher    = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
      reportid     = {FZJ-2025-04205},
      isbn         = {978-3-95806-856-8},
      series       = {Schriften des Forschungszentrums Jülich Reihe
                      Schlüsseltechnologien / Key Technologies},
      pages        = {iv, 211},
      year         = {2025},
      note         = {Dissertation, Köln, 2025},
      abstract     = {In this work, we first consider standard quantum control
                      problems for superconducting transmon qubits. Such quantum
                      systems have been studied extensively both from an
                      analytical and a numerical perspective $[MSG\&al11;$ TMW16].
                      Analytical pulses usually show dependencies from system
                      parameters that are not easily found in numerical solutions.
                      Therefore, our approach is then to employ neural networks to
                      learn the functional dependence of optimal quantum control
                      solutions from system parameters. We show that we can
                      optimize such solutions analytically for single and
                      two-qubit gates using either very few parameter samples or
                      large numbers of pulse frequencies for large parameter
                      ranges. Afterward, we move to a higher level of
                      optimization. We consider variational gates in trapped-ion
                      quantum computers $[MMN\&al16].$ In this setting, we employ
                      a hybrid scheme that uses both reinforcement learning and
                      continuous optimization to optimize both circuit structure
                      and variational angles concurrently $[BDS\&al18;$
                      $SEL\&al22].$ We show that our reinforcement learning
                      algorithm assisted by a continuous optimizer can construct
                      effective solutions to the gate synthesis problem that
                      matches and surpasses standard circuit compilers. Next, we
                      consider variational optimization of non-linear maps
                      $[HCS\&al23]$ acting on quantum states such as entanglement
                      purification protocols. An entanglement purification
                      protocol acts as a highly non-linear map, but it still
                      outputs a faithful representation of a quantum system.
                      Optimizing it for a specific family of states requires
                      reducing the number of operations needed to retrieve the
                      original state. This optimization has the potential to make
                      purification protocols easier to implement on a quantum
                      device. In fact, the exponential scaling of the purification
                      protocol with the number of states [DB07] implies that such
                      protocols may not be applied directly without careful
                      engineering. We also show that the performance of standard
                      purification protocols for arbitrary two-qubit input states
                      leads to poor output values of the concurrence. Our
                      optimized protocols prove instead able to increase the value
                      of the concurrence above the maximum limit of traditional
                      analytical protocols. We also show how the twirling
                      operation becomes an obstacle to the performance of the
                      protocol itself when considering random two-qubit states,
                      although it is a useful tool in the design of entanglement
                      purification protocols. Finally, we study parameter sampling
                      in quantum circuits, focusing in particular on the LCU
                      methods. Such problems are particularly interesting for
                      meta-variational settings [CKA21] where we compute the
                      average over observables evaluated at different points in
                      the parameter space. They are also relevant for optimal
                      quantum control algorithms because the computation of the
                      fidelity with respect to a target operation is the basis of
                      most optimal quantum control routines. In conclusion, we
                      analyze several optimization problems that are relevant for
                      quantum science and technology. We show that machine
                      learning-assisted solutions can be applied successfully to
                      engineer optimal quantum control pulses and compilation
                      strategies based on variational angles. Also in the context
                      of entanglement purification, we show how our optimized
                      protocols can surpass current strategies for multiparametric
                      families of states.},
      cin          = {PGI-8},
      cid          = {I:(DE-Juel1)PGI-8-20190808},
      pnm          = {899 - ohne Topic (POF4-899)},
      pid          = {G:(DE-HGF)POF4-899},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:0001-2602091313079.627807618957},
      doi          = {10.34734/FZJ-2025-04205},
      url          = {https://juser.fz-juelich.de/record/1047289},
}