% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{Hader:1047275,
      author       = {Hader, Fabian and Fuchs, Fabian and Fleitmann, Sarah and
                      Havemann, Karin and Scherer, Benedikt and Vogelbruch,
                      Jan-Friedrich and Geck, Lotte and van Waasen, Stefan},
      title        = {{T}owards {S}calable {C}ryogenic {C}harge {T}ransition
                      {D}etection for {A}utomated {Q}uantum {D}ot {T}uning},
      reportid     = {FZJ-2025-04196},
      year         = {2025},
      abstract     = {A scalable platform for quantum computing necessitates the
                      automation of the quantum dot tuning process. One crucial
                      step in this process is the capture of the requisite number
                      of electrons within the quantum dots. This is typically
                      accomplished through the analysis of charge stability
                      diagrams (CSDs), wherein the charge transitions manifest as
                      edges. Therefore, it is imperative to automatically
                      recognize these edges with high reliability. To reduce the
                      amount of data transferred to the room-temperature
                      electronics, it is optimal to integrate this detection
                      locally at the cryogenic stage. Machine learning methods for
                      the charge transition detection necessitate substantial
                      amounts of labelled data for training and testing purposes.
                      Therefore, we developed SimCATS [1], a novel approach to the
                      realistic simulation of such data. It enables the simulation
                      of ideal CSD data, complemented by appropriate sensor
                      responses and distortions. The simulated data facilitates
                      the investigation and training of potential charge
                      transition detection methods. Afterward, the trained
                      detection methods are quantitatively and qualitatively
                      evaluated using simulated and experimentally measured data
                      from a GaAs and a SiGe qubit sample. Subsequent exploration
                      of model size reduction revealed a strong correlation with
                      the complexity of the data analysis task, which was
                      mitigated through the implementation of sensor dot
                      compensation. In conjunction with superior measurement
                      quality, this compensation enables robust and scalable
                      ray-based (1D) charge transition detection. Finally, we
                      estimate the cryogenic power requirements for the
                      application of this approach to a fully automated,
                      one-million-qubit system. <br>[1] F. Hader et al. Simulation
                      of Charge Stability Diagrams for Automated Tuning Solutions
                      (SimCATS), IEEE Transactions on Quantum Engineering, doi:
                      10.1109/TQE.2024.3445967 (2024)<br>[2] F. Hader et al.
                      SimCATS GitHub repository,
                      https://github.com/f-hader/SimCATS (2023)},
      month         = {Oct},
      date          = {2025-10-06},
      organization  = {Silicon Quantum Electronics Workshop,
                       Los Angeles (USA), 6 Oct 2025 - 8 Oct
                       2025},
      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-2025-04196},
      url          = {https://juser.fz-juelich.de/record/1047275},
}