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@INPROCEEDINGS{Papajewski:1055112,
      author       = {Papajewski, Benjamin and Fleitmann, Sarah and Hader, Fabian
                      and Havemann, Karin and Vogelbruch, Jan-Friedrich and
                      Humpohl, Simon and van Waasen, Stefan},
      title        = {{S}ensor {S}can {S}imulation for {A}utomated {T}uning of
                      {G}ate-{D}efined {S}emiconductor {Q}uantum {D}ots},
      reportid     = {FZJ-2026-01869},
      year         = {2026},
      abstract     = {Precise tuning of semiconductor quantum dots is essential
                      for their operation as qubits. During this process, sensor
                      scans measure the response of the sensor dots coupled to
                      quantum dots, revealing optimal operating conditions.
                      Because experimental data are time-intensive to obtain and
                      label, progress in data-driven tuning methods has been
                      limited. Simulated sensor scans provide a controllable and
                      reproducible framework for developing, testing, and
                      benchmarking a wide range of tuning algorithms under
                      well-defined conditions.To address this need, we developed a
                      simulation algorithm that generates realistic sensor scans.
                      The algorithm was implemented as a modular extension of the
                      SimCATS framework. Using empirical and phenomenological
                      modeling, it reproduces the sensor’s response to gate
                      voltages based on the corresponding lever arms, which depend
                      on the gate layout. The sensor response is modeled through
                      an equivalent circuit, where the two barriers and the sensor
                      dot are represented as three resistors in series. The
                      resulting simulated scans reproduce characteristic features
                      such as Coulomb peaks. Furthermore, the simulation
                      incorporates configurable distortions such as white noise,
                      pink noise, random telegraph noise, and peak deformations to
                      enhance realism. The algorithm will be released as an
                      open-source Python package on GitHub and made easily
                      accessible for researchers via PyPI.The simulation
                      parameters can be defined manually by the user or
                      automatically generated through built-in samplers that
                      produce randomized parameter sets tailored to a specific
                      setup. For instance, a dedicated sampler for GaAs/AlGaAs
                      heterostructures is included. Such samplers enable the rapid
                      generation of large, labeled, and diverse datasets for
                      training and evaluating automated tuning algorithms. The
                      algorithm’s ability to replicate experimental measurements
                      was evaluated by generating datasets that mimic the sensor
                      behavior observed in GaAs/AlGaAs heterostructures.
                      Parameter-extraction methods were developed and applied to
                      experimental data to obtain realistic simulation inputs. The
                      resulting simulated datasets were compared with actual
                      experimental measurements to validate the simulation’s
                      accuracy. The simulated datasets were evaluated using
                      quantitative and qualitative methods.By providing labeled
                      and realistically distorted sensor scans, our work enables
                      the development and validation of automated tuning
                      algorithms. The simulated data are currently used to develop
                      and test algorithms that automatically identify the Coulomb
                      oscillation area in sensor scans. These algorithms can
                      afterward be applied and tested on real experimental data.
                      This demonstrates how the simulation contributes to the
                      advancement of scalable quantum computing.},
      month         = {Mar},
      date          = {2026-03-02},
      organization  = {deRSE26 - 6th conference for Research
                       Software Engineering $\&$ 1st Stuttgart
                       Research Software Day, Stuttgart
                       (Germany), 2 Mar 2026 - 5 Mar 2026},
      subtyp        = {Other},
      cin          = {PGI-4},
      cid          = {I:(DE-Juel1)PGI-4-20110106},
      pnm          = {5223 - Quantum-Computer Control Systems and Cryoelectronics
                      (POF4-522)},
      pid          = {G:(DE-HGF)POF4-5223},
      typ          = {PUB:(DE-HGF)24},
      doi          = {10.34734/FZJ-2026-01869},
      url          = {https://juser.fz-juelich.de/record/1055112},
}