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001047275 0247_ $$2datacite_doi$$a10.34734/FZJ-2025-04196
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001047275 1001_ $$0P:(DE-Juel1)170099$$aHader, Fabian$$b0$$eCorresponding author$$ufzj
001047275 1112_ $$aSilicon Quantum Electronics Workshop$$cLos Angeles$$d2025-10-06 - 2025-10-08$$gSiQEW 2025$$wUSA
001047275 245__ $$aTowards Scalable Cryogenic Charge Transition Detection for Automated Quantum Dot Tuning
001047275 260__ $$c2025
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001047275 520__ $$aA 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. <br>[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)<br>[2] F. Hader et al. SimCATS GitHub repository, https://github.com/f-hader/SimCATS (2023)
001047275 536__ $$0G:(DE-HGF)POF4-5223$$a5223 - Quantum-Computer Control Systems and Cryoelectronics (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001047275 7001_ $$0P:(DE-Juel1)176540$$aFuchs, Fabian$$b1
001047275 7001_ $$0P:(DE-Juel1)173094$$aFleitmann, Sarah$$b2$$ufzj
001047275 7001_ $$0P:(DE-Juel1)201385$$aHavemann, Karin$$b3$$ufzj
001047275 7001_ $$0P:(DE-Juel1)173093$$aScherer, Benedikt$$b4$$ufzj
001047275 7001_ $$0P:(DE-Juel1)133952$$aVogelbruch, Jan-Friedrich$$b5$$ufzj
001047275 7001_ $$0P:(DE-Juel1)169123$$aGeck, Lotte$$b6$$ufzj
001047275 7001_ $$0P:(DE-Juel1)142562$$avan Waasen, Stefan$$b7$$ufzj
001047275 8564_ $$uhttps://juser.fz-juelich.de/record/1047275/files/Hader_TowardsScalableCryogenicChargeTransitionDetectionForAutomatedQuantumDotTuning.pdf$$yOpenAccess
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