Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning
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. 2024. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. 14, 17281.
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Author
Severin, B.;
Lennon, D. T.;
Camenzind, L. C.;
Vigneau, F.;
Fedele, F.;
Jirovec, DanielISTA 
;
Ballabio, A.;
Chrastina, D.;
Isella, G.;
de Kruijf, M.;
Carballido, M. J.;
Svab, S.
All
;
Ballabio, A.;
Chrastina, D.;
Isella, G.;
de Kruijf, M.;
Carballido, M. J.;
Svab, S.
All
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Grant
Abstract
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.
Publishing Year
Date Published
2024-07-27
Journal Title
Scientific Reports
Publisher
Springer Nature
Acknowledgement
We acknowledge Ang Li, Erik P. A. M. Bakkers (University of Eindhoven) for the fabrication of the Ge/Si nanowire. This work was supported by the Royal Society, the EPSRC National Quantum Technology Hub in Networked Quantum Information Technology (EP/M013243/1), Quantum Technology Capital (EP/N014995/1), EPSRC Platform Grant (EP/R029229/1), the European Research Council (Grant agreement 948932), the Swiss Nanoscience Institute, the NCCR SPIN, the EU H2020 European Microkelvin Platform EMP grant No. 824109, the Scientific Service Units of IST Austria through resources provided by the nanofabrication facility, the FWF-I 05060 and the FWF-P 30207 project.
Acknowledged SSUs
Volume
14
Article Number
17281
ISSN
IST-REx-ID
Cite this
Severin B, Lennon DT, Camenzind LC, et al. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. 2024;14. doi:10.1038/s41598-024-67787-z
Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., … Ares, N. (2024). Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. Springer Nature. https://doi.org/10.1038/s41598-024-67787-z
Severin, B., D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, Daniel Jirovec, A. Ballabio, et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” Scientific Reports. Springer Nature, 2024. https://doi.org/10.1038/s41598-024-67787-z.
B. Severin et al., “Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning,” Scientific Reports, vol. 14. Springer Nature, 2024.
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. 2024. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. Scientific Reports. 14, 17281.
Severin, B., et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” Scientific Reports, vol. 14, 17281, Springer Nature, 2024, doi:10.1038/s41598-024-67787-z.
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