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001032505 0247_ $$2datacite_doi$$a10.34734/FZJ-2024-06298
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001032505 1001_ $$0P:(DE-Juel1)170099$$aHader, Fabian$$b0
001032505 1112_ $$aSpinQubit 6$$cSydney$$d2024-11-04 - 2024-11-08$$wAustralia
001032505 245__ $$aQuantum Dot CSD Simulation and Automated Charge Transition Detection
001032505 260__ $$c2024
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001032505 520__ $$aA 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.
001032505 536__ $$0G:(DE-HGF)POF4-5223$$a5223 - Quantum-Computer Control Systems and Cryoelectronics (POF4-522)$$cPOF4-522$$fPOF IV$$x0
001032505 7001_ $$0P:(DE-Juel1)176540$$aFuchs, Fabian$$b1
001032505 7001_ $$0P:(DE-Juel1)173094$$aFleitmann, Sarah$$b2
001032505 7001_ $$0P:(DE-Juel1)201385$$aHavemann, Karin$$b3
001032505 7001_ $$0P:(DE-Juel1)173093$$aScherer, Benedikt$$b4
001032505 7001_ $$0P:(DE-Juel1)133952$$aVogelbruch, Jan-Friedrich$$b5
001032505 7001_ $$0P:(DE-Juel1)169123$$aGeck, Lotte$$b6
001032505 7001_ $$0P:(DE-Juel1)142562$$avan Waasen, Stefan$$b7
001032505 8564_ $$uhttps://juser.fz-juelich.de/record/1032505/files/Quantum%20Dot%20CSD%20Simulation%20and%20Automated%20Charge%20Transition%20Detection%20%5BPoster%5D%5B2024%5D.pdf$$yOpenAccess
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