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@PHDTHESIS{Weber:642287,
      author       = {Weber, Tom},
      othercontributors = {Borras, Kerstin and Mathey, Ludwig and Schäfer, Ina},
      title        = {{C}onstructing and {B}enchmarking {N}oise {M}odels for
                      {Q}uantum {C}omputing},
      school       = {University of Hamburg},
      type         = {Dissertation},
      reportid     = {PUBDB-2025-05458},
      pages        = {153},
      year         = {2024},
      note         = {Dissertation, University of Hamburg, 2024},
      abstract     = {Context: The main obstacle of quantum computing on its way
                      to scalability is the noisy nature of current hardware.
                      Various types of errors lead to incorrect computational
                      results,and measures such as quantum error mitigation are
                      needed to counteract these errors. Many error mitigation
                      techniques require a thorough understanding of the noise and
                      its impact on computations. Therefore, accurate noise models
                      are indispensable to facilitate meaningful quantum computing
                      in the near future. They also enable realistic simulations
                      of quantum devices. Since these devices are currently
                      limited and quantum computing is expensive, researchers
                      typically rely on simulations for testing their
                      algorithms.Objective: In this thesis, we aim to construct a
                      realistic noise model for quantum computing and optimise its
                      model parameters. The model construction should build on the
                      underlying physical processes, and the number of parameters
                      should scale well with the system size. We also address the
                      evaluation and comparison of different noise models by
                      benchmarks using quantum circuits specific to real-world
                      applications. Finally, we aim to develop useful graphical
                      representations of noise models based on the existing
                      quantum circuit model.Method: We present a benchmarking
                      framework for quantum computing noise models and evaluate it
                      based on benchmarking quality attributes from the
                      literature. Moreover, we construct a quantum computing noise
                      model and develop a training procedure for its parameters.
                      We analyse the parameters of the trained noise model and the
                      impact ofdifferent types of errors on computations. We
                      benchmark the noise model with the above approach and
                      compare it to a noise model provided by IBM’s software
                      development kit Qiskit. Finally, we develop an extension of
                      the quantum circuit model to represent noise
                      channels.Result: The benchmarks performed in this thesis
                      show that our noise model predicts noisy hardware behaviour
                      of IBM’s $ibmq_manila$ quantum device equally well as the
                      Qiskit model, if not better, based on quantum circuits
                      commonly used for variational quantum algorithms. Within the
                      trained noise model, readout error has the most detrimental
                      impact on computations. Our benchmarking approach satisfies
                      relevant quality criteria by choosing suitable quantum
                      circuits and objective functions to compare model
                      predictionsto hardware data. Moreover, the quantum circuit
                      model can be extended meaningfully by noise
                      channels.Conclusion: Our benchmarking approach is suitable
                      for evaluating and comparing quantum computing noise models.
                      In future work, volumetric benchmarks should be performed
                      for various noise models and application contexts. The
                      parameter optimisation based on learning with training
                      circuits yields accurate noise models and shows the
                      importance of mitigating measurement error. Different
                      optimisation algorithms and quantum circuits should be
                      explored, and more types of noise should be incorporated
                      into the noise model.},
      cin          = {CMS},
      cid          = {I:(DE-H253)CMS-20120731},
      pnm          = {611 - Fundamental Particles and Forces (POF4-611) /
                      HIDSS-0002 - DASHH: Data Science in Hamburg - Helmholtz
                      Graduate School for the Structure of Matter
                      $(2019_IVF-HIDSS-0002)$},
      pid          = {G:(DE-HGF)POF4-611 / $G:(DE-HGF)2019_IVF-HIDSS-0002$},
      experiment   = {EXP:(DE-MLZ)NOSPEC-20140101},
      typ          = {PUB:(DE-HGF)11},
      urn          = {urn:nbn:de:gbv:18-ediss-125184},
      doi          = {10.3204/PUBDB-2025-05458},
      url          = {https://bib-pubdb1.desy.de/record/642287},
}