Back to Search
Start Over
Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning.
- Source :
- Scientific Reports; 7/27/2024, Vol. 14 Issue 1, p1-10, 10p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- NANOWIRE devices
MACHINE learning
QUANTUM dot devices
QUANTUM dots
Subjects
Details
- Language :
- English
- ISSN :
- 20452322
- Volume :
- 14
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Scientific Reports
- Publication Type :
- Academic Journal
- Accession number :
- 178624093
- Full Text :
- https://doi.org/10.1038/s41598-024-67787-z