Cross-architecture tuning using machine learning

This paper* illustrates that a machine can successfully learn to tune three very different architectures for quantum devices, without requiring re-programming.

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.  

In this paper the authors, headed by Dr Brandon Severin, 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. 

They achieved tuning times of 30, 10 and 92 minutes respectively.  The algorithm also provides insight into the parameter space landscape for each of these devices, allowing for characterisation 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.

* 'Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning', as published in Nature Scientific Reports.