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@INPROCEEDINGS{Hader:1032505,
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 = {{Q}uantum {D}ot {CSD} {S}imulation and {A}utomated {C}harge
{T}ransition {D}etection},
reportid = {FZJ-2024-06298},
year = {2024},
abstract = {A scalable platform for quantum computing requires the
automation of the quantum dot tuning process. One crucial
step is to trap the appropriate number of electrons in the
quantum dots typically accomplished by analyzing charge
stability diagrams (CSDs), in which the charge transitions
manifest as edges. Hence, it is necessary to recognize these
edges automatically and reliably. Machine learning methods
for this purpose require large amounts of data for training
and testing. Therefore, we introduce SimCATS, a new approach
to the realistic simulation of such data. It enables the
simulation of ideal CSD data complemented with appropriate
sensor responses and distortions. This enables us to
investigate possible edge detection methods, train them with
the simulated data, and carry out a quantitative and
qualitative comparison on simulated and experimentally
measured data from a GaAs and a SiGe qubit sample.},
month = {Nov},
date = {2024-11-04},
organization = {SpinQubit 6, Sydney (Australia), 4 Nov
2024 - 8 Nov 2024},
subtyp = {Other},
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)24},
doi = {10.34734/FZJ-2024-06298},
url = {https://juser.fz-juelich.de/record/1032505},
}