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| Hauptseite > Publikationsdatenbank > Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams > print |
| Hauptseite > Publikationsdatenbank > Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams > print |
| 001 | 1034092 | ||
| 005 | 20250129092416.0 | ||
| 024 | 7 | _ | |a 10.36227/techrxiv.172963185.53119182/v1 |2 doi |
| 037 | _ | _ | |a FZJ-2024-06913 |
| 100 | 1 | _ | |a Hader, Fabian |0 P:(DE-Juel1)170099 |b 0 |
| 245 | _ | _ | |a Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams |
| 260 | _ | _ | |c 2024 |
| 336 | 7 | _ | |a Preprint |b preprint |m preprint |0 PUB:(DE-HGF)25 |s 1734423165_28897 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a WORKING_PAPER |2 ORCID |
| 336 | 7 | _ | |a Electronic Article |0 28 |2 EndNote |
| 336 | 7 | _ | |a preprint |2 DRIVER |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a Output Types/Working Paper |2 DataCite |
| 520 | _ | _ | |a Gate-defined semiconductor quantum dots require an appropriate number of electrons to function as qubits. The number of electrons is usually tuned by analyzing charge stability diagrams, in which charge transitions manifest as edges. Therefore, to fully automate qubit tuning, it is necessary to recognize these edges automatically and reliably. This paper investigates possible detection methods, describes their training with simulated data from the SimCATS framework, and performs a quantitative comparison with a future hardware implementation in mind. Furthermore, we investigated the quality of the optimized approaches on experimentally measured data from a GaAs and a SiGe qubit sample. |
| 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 |
| 588 | _ | _ | |a Dataset connected to CrossRef |
| 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 |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 Waasen, Stefan Van |0 P:(DE-Juel1)142562 |b 7 |u fzj |
| 773 | _ | _ | |a 10.36227/techrxiv.172963185.53119182/v1 |
| 909 | C | O | |o oai:juser.fz-juelich.de:1034092 |p VDB |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)170099 |
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| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 3 |6 P:(DE-Juel1)201385 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 4 |6 P:(DE-Juel1)173093 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 5 |6 P:(DE-Juel1)133952 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 6 |6 P:(DE-Juel1)169123 |
| 910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 7 |6 P:(DE-Juel1)142562 |
| 913 | 1 | _ | |a DE-HGF |b Key Technologies |l Natural, Artificial and Cognitive Information Processing |1 G:(DE-HGF)POF4-520 |0 G:(DE-HGF)POF4-522 |3 G:(DE-HGF)POF4 |2 G:(DE-HGF)POF4-500 |4 G:(DE-HGF)POF |v Quantum Computing |9 G:(DE-HGF)POF4-5223 |x 0 |
| 914 | 1 | _ | |y 2024 |
| 920 | _ | _ | |l yes |
| 920 | 1 | _ | |0 I:(DE-Juel1)ZEA-2-20090406 |k ZEA-2 |l Zentralinstitut für Elektronik |x 0 |
| 980 | _ | _ | |a preprint |
| 980 | _ | _ | |a VDB |
| 980 | _ | _ | |a I:(DE-Juel1)ZEA-2-20090406 |
| 980 | _ | _ | |a UNRESTRICTED |
| 981 | _ | _ | |a I:(DE-Juel1)PGI-4-20110106 |
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