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| Hauptseite > Publikationsdatenbank > Towards Scalable Cryogenic Charge Transition Detection for Automated Quantum Dot Tuning > print |
| Hauptseite > Publikationsdatenbank > Towards Scalable Cryogenic Charge Transition Detection for Automated Quantum Dot Tuning > print |
| 001 | 1047275 | ||
| 005 | 20251023202111.0 | ||
| 024 | 7 | _ | |a 10.34734/FZJ-2025-04196 |2 datacite_doi |
| 037 | _ | _ | |a FZJ-2025-04196 |
| 041 | _ | _ | |a English |
| 100 | 1 | _ | |a Hader, Fabian |0 P:(DE-Juel1)170099 |b 0 |e Corresponding author |u fzj |
| 111 | 2 | _ | |a Silicon Quantum Electronics Workshop |g SiQEW 2025 |c Los Angeles |d 2025-10-06 - 2025-10-08 |w USA |
| 245 | _ | _ | |a Towards Scalable Cryogenic Charge Transition Detection for Automated Quantum Dot Tuning |
| 260 | _ | _ | |c 2025 |
| 336 | 7 | _ | |a Conference Paper |0 33 |2 EndNote |
| 336 | 7 | _ | |a INPROCEEDINGS |2 BibTeX |
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| 520 | _ | _ | |a A scalable platform for quantum computing necessitates the automation of the quantum dot tuning process. One crucial step in this process is the capture of the requisite number of electrons within the quantum dots. This is typically accomplished through the analysis of charge stability diagrams (CSDs), wherein the charge transitions manifest as edges. Therefore, it is imperative to automatically recognize these edges with high reliability. To reduce the amount of data transferred to the room-temperature electronics, it is optimal to integrate this detection locally at the cryogenic stage. Machine learning methods for the charge transition detection necessitate substantial amounts of labelled data for training and testing purposes. Therefore, we developed SimCATS [1], a novel approach to the realistic simulation of such data. It enables the simulation of ideal CSD data, complemented by appropriate sensor responses and distortions. The simulated data facilitates the investigation and training of potential charge transition detection methods. Afterward, the trained detection methods are quantitatively and qualitatively evaluated using simulated and experimentally measured data from a GaAs and a SiGe qubit sample. Subsequent exploration of model size reduction revealed a strong correlation with the complexity of the data analysis task, which was mitigated through the implementation of sensor dot compensation. In conjunction with superior measurement quality, this compensation enables robust and scalable ray-based (1D) charge transition detection. Finally, we estimate the cryogenic power requirements for the application of this approach to a fully automated, one-million-qubit system. [1] F. Hader et al. Simulation of Charge Stability Diagrams for Automated Tuning Solutions (SimCATS), IEEE Transactions on Quantum Engineering, doi: 10.1109/TQE.2024.3445967 (2024) [2] F. Hader et al. SimCATS GitHub repository, https://github.com/f-hader/SimCATS (2023) |
| 536 | _ | _ | |a 5223 - Quantum-Computer Control Systems and Cryoelectronics (POF4-522) |0 G:(DE-HGF)POF4-5223 |c POF4-522 |f POF IV |x 0 |
| 700 | 1 | _ | |a Fuchs, Fabian |0 P:(DE-Juel1)176540 |b 1 |
| 700 | 1 | _ | |a Fleitmann, Sarah |0 P:(DE-Juel1)173094 |b 2 |u fzj |
| 700 | 1 | _ | |a Havemann, Karin |0 P:(DE-Juel1)201385 |b 3 |u fzj |
| 700 | 1 | _ | |a Scherer, Benedikt |0 P:(DE-Juel1)173093 |b 4 |u fzj |
| 700 | 1 | _ | |a Vogelbruch, Jan-Friedrich |0 P:(DE-Juel1)133952 |b 5 |u fzj |
| 700 | 1 | _ | |a Geck, Lotte |0 P:(DE-Juel1)169123 |b 6 |u fzj |
| 700 | 1 | _ | |a van Waasen, Stefan |0 P:(DE-Juel1)142562 |b 7 |u fzj |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/1047275/files/Hader_TowardsScalableCryogenicChargeTransitionDetectionForAutomatedQuantumDotTuning.pdf |y OpenAccess |
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