Home > Publications database > Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams > print |
001 | 1044873 | ||
005 | 20250930132715.0 | ||
024 | 7 | _ | |a 10.1109/TQE.2025.3596392 |2 doi |
024 | 7 | _ | |a WOS:001569673100001 |2 WOS |
037 | _ | _ | |a FZJ-2025-03407 |
082 | _ | _ | |a 621.3 |
100 | 1 | _ | |a Hader, Fabian |0 P:(DE-Juel1)170099 |b 0 |e Corresponding author |u fzj |
245 | _ | _ | |a Automated Charge Transition Detection in Quantum Dot Charge Stability Diagrams |
260 | _ | _ | |a New York, NY |c 2025 |b IEEE |
336 | 7 | _ | |a article |2 DRIVER |
336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1758524361_11804 |2 PUB:(DE-HGF) |
336 | 7 | _ | |a ARTICLE |2 BibTeX |
336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
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 article 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 DataCite |
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 10.1109/TQE.2025.3596392 |g Vol. 6, p. 1 - 14 |0 PERI:(DE-600)3035782-2 |p 5500414 |t IEEE transactions on quantum engineering |v 6 |y 2025 |x 2689-1808 |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1044873/files/APC600697565.pdf |
856 | 4 | _ | |u https://juser.fz-juelich.de/record/1044873/files/Automated_Charge_Transition_Detection_in_Quantum_Dot_Charge_Stability_Diagrams.pdf |y Restricted |
909 | C | O | |o oai:juser.fz-juelich.de:1044873 |p VDB |p OpenAPC |p openCost |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 0 |6 P:(DE-Juel1)170099 |
910 | 1 | _ | |a Forschungszentrum Jülich |0 I:(DE-588b)5008462-8 |k FZJ |b 2 |6 P:(DE-Juel1)173094 |
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 2025 |
915 | p | c | |a APC keys set |0 PC:(DE-HGF)0000 |2 APC |
915 | p | c | |a DOAJ Journal |0 PC:(DE-HGF)0003 |2 APC |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0200 |2 StatID |b SCOPUS |d 2024-12-12 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0300 |2 StatID |b Medline |d 2024-12-12 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0501 |2 StatID |b DOAJ Seal |d 2024-04-03T10:39:05Z |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0500 |2 StatID |b DOAJ |d 2024-04-03T10:39:05Z |
915 | _ | _ | |a Peer Review |0 StatID:(DE-HGF)0030 |2 StatID |b DOAJ : Anonymous peer review |d 2024-04-03T10:39:05Z |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0199 |2 StatID |b Clarivate Analytics Master Journal List |d 2024-12-12 |
915 | _ | _ | |a WoS |0 StatID:(DE-HGF)0112 |2 StatID |b Emerging Sources Citation Index |d 2024-12-12 |
915 | _ | _ | |a DBCoverage |0 StatID:(DE-HGF)0150 |2 StatID |b Web of Science Core Collection |d 2024-12-12 |
915 | _ | _ | |a Article Processing Charges |0 StatID:(DE-HGF)0561 |2 StatID |d 2024-12-12 |
915 | _ | _ | |a Fees |0 StatID:(DE-HGF)0700 |2 StatID |d 2024-12-12 |
920 | 1 | _ | |0 I:(DE-Juel1)PGI-4-20110106 |k PGI-4 |l Integrated Computing Architectures |x 0 |
980 | _ | _ | |a journal |
980 | _ | _ | |a VDB |
980 | _ | _ | |a I:(DE-Juel1)PGI-4-20110106 |
980 | _ | _ | |a APC |
980 | _ | _ | |a UNRESTRICTED |
980 | 1 | _ | |a APC |
Library | Collection | CLSMajor | CLSMinor | Language | Author |
---|