TY  - CONF
AU  - Hader, Fabian
AU  - Fuchs, Fabian
AU  - Fleitmann, Sarah
AU  - Havemann, Karin
AU  - Scherer, Benedikt
AU  - Vogelbruch, Jan-Friedrich
AU  - Geck, Lotte
AU  - van Waasen, Stefan
TI  - Quantum Dot CSD Simulation and Automated Charge Transition Detection
M1  - FZJ-2024-06298
PY  - 2024
AB  - 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.
T2  - SpinQubit 6
CY  - 4 Nov 2024 - 8 Nov 2024, Sydney (Australia)
Y2  - 4 Nov 2024 - 8 Nov 2024
M2  - Sydney, Australia
LB  - PUB:(DE-HGF)24
DO  - DOI:10.34734/FZJ-2024-06298
UR  - https://juser.fz-juelich.de/record/1032505
ER  -