Gast ::
| Hauptseite > Publikationsdatenbank > Quantum Dot CSD Simulation and Automated Charge Transition Detection |
| Hauptseite > Publikationsdatenbank > Quantum Dot CSD Simulation and Automated Charge Transition Detection |
| Poster (Other) | FZJ-2024-06298 |
; ; ; ; ; ; ;
2024
This record in other databases:
Please use a persistent id in citations: doi:10.34734/FZJ-2024-06298
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.
|
The record appears in these collections: |
PDF