  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. We give a key step towards tackling this variability with
an algorithm that, without modification, is capable of tuning a 4-gate Si
FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum
dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes,
respectively. The algorithm also provides insight into the parameter space
landscape for each of these devices. These results show that overarching
solutions for the tuning of quantum devices are enabled by machine learning.
