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@MASTERSTHESIS{Fleitmann:1021177,
author = {Fleitmann, Sarah},
title = {{C}haracterization of {D}istortions in {C}harge {S}tability
{D}iagrams and {T}heir {S}imulation in {M}odeled {D}ata},
volume = {4444},
school = {FH Aachen},
type = {Masterarbeit},
address = {Jülich},
publisher = {Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag},
reportid = {FZJ-2024-00621, 4444},
series = {Berichte des Forschungszentrums Jülich},
pages = {84},
year = {2024},
note = {Masterarbeit, FH Aachen, 2022},
abstract = {Charge stability diagrams provide information about the
electron occupation of double quantum dots. They are used
during the tuning process of double quantum dots, necessary
to enable their operation as quantum bits. Simulated charge
stability diagrams are required for testing and developing
automated tuning algorithms. They are well suited for that
because they can be generated fast and the ground truth
occupation is known for a simulated data point. In contrast,
the measuring of experimental datasets takes a long time and
the ground truth is unknown. This thesis deals with the
simulation of distortions in simulated charge stability
diagrams. For the simulation of the undisturbed occupation
data, the capacitive model [1] and the Hubbard model [2] are
presented. However, both models are not suited for the
simulation of the honeycomb structures visible in available
experimental charge stability diagrams. Another approach,
currently developed by Fabian Hader from the ZEA-2, is used
to overcome this problem.To simulate realistic charge
stability diagrams, the sensor response including
distortions has to be added to the clean occupation data.
Five types of distortions are identified: cross-coupling
between sensor and double dot plunger gates, white noise,
pink noise,random telegraph noise, and dot jumps. For a
realistic simulation of these, procedures to determine
parameter ranges from the experimental charge stability
diagrams are developed and applied. Then, the generated
simulated dataset is evaluated visually and by different
metrics. To improve the quality of the simulated dataset,
the initial parameters are adjusted, and the simulation
model itself is refined. Finally, the optimized simulated
dataset is evaluated with the same metrics, and the results
are discussed.},
cin = {ZEA-2},
cid = {I:(DE-Juel1)ZEA-2-20090406},
pnm = {5223 - Quantum-Computer Control Systems and Cryoelectronics
(POF4-522)},
pid = {G:(DE-HGF)POF4-5223},
typ = {PUB:(DE-HGF)3 / PUB:(DE-HGF)19},
doi = {10.34734/FZJ-2024-00621},
url = {https://juser.fz-juelich.de/record/1021177},
}