Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning
The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-ga...
| Main Authors: | , , , , , , , , , , , , , , , , , , , , , |
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| Format: | Journal article |
| Language: | English |
| Published: |
Nature Research
2024
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| _version_ | 1826313720501895168 |
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| author | Severin, B Lennon, DT Camenzind, LC Vigneau, F Fedele, F Jirovec, D Ballabio, A Chrastina, D Isella, G de Kruijf, M Carballido, MJ Svab, S Kuhlmann, AV Geyer, S Froning, FNM Moon, H Osborne, MA Sejdinovic, D Katsaros, G Zumbühl, DM Briggs, GAD Ares, N |
| author_facet | Severin, B Lennon, DT Camenzind, LC Vigneau, F Fedele, F Jirovec, D Ballabio, A Chrastina, D Isella, G de Kruijf, M Carballido, MJ Svab, S Kuhlmann, AV Geyer, S Froning, FNM Moon, H Osborne, MA Sejdinovic, D Katsaros, G Zumbühl, DM Briggs, GAD Ares, N |
| author_sort | Severin, B |
| collection | OXFORD |
| description | The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning. |
| first_indexed | 2024-09-25T04:19:24Z |
| format | Journal article |
| id | oxford-uuid:be31e5e1-835a-4147-8ec3-e00b8fec7e2a |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:19:24Z |
| publishDate | 2024 |
| publisher | Nature Research |
| record_format | dspace |
| spelling | oxford-uuid:be31e5e1-835a-4147-8ec3-e00b8fec7e2a2024-07-27T19:38:20ZCross-architecture tuning of silicon and SiGe-based quantum devices using machine learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:be31e5e1-835a-4147-8ec3-e00b8fec7e2aEnglishJisc Publications RouterNature Research2024Severin, BLennon, DTCamenzind, LCVigneau, FFedele, FJirovec, DBallabio, AChrastina, DIsella, Gde Kruijf, MCarballido, MJSvab, SKuhlmann, AVGeyer, SFroning, FNMMoon, HOsborne, MASejdinovic, DKatsaros, GZumbühl, DMBriggs, GADAres, NThe potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions and each device realisation requires a different tuning protocol. We demonstrate that it is possible to automate the tuning of a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate Ge/SiGe heterostructure double quantum dot device from scratch with the same algorithm. We achieve tuning times of 30, 10, and 92 min, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for the characterization of the regions where double quantum dot regimes are found. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning. |
| spellingShingle | Severin, B Lennon, DT Camenzind, LC Vigneau, F Fedele, F Jirovec, D Ballabio, A Chrastina, D Isella, G de Kruijf, M Carballido, MJ Svab, S Kuhlmann, AV Geyer, S Froning, FNM Moon, H Osborne, MA Sejdinovic, D Katsaros, G Zumbühl, DM Briggs, GAD Ares, N Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title | Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title_full | Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title_fullStr | Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title_full_unstemmed | Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title_short | Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning |
| title_sort | cross architecture tuning of silicon and sige based quantum devices using machine learning |
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