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| Hauptseite > Publikationsdatenbank > Characterization of Distortions in Charge Stability Diagrams and Their Simulation in Modeled Data |
| Book/Master Thesis | FZJ-2024-00621 |
2024
Forschungszentrum Jülich GmbH Zentralbibliothek, Verlag
Jülich
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Please use a persistent id in citations: doi:10.34734/FZJ-2024-00621
Report No.: 4444
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.
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