  Device variability is a bottleneck for the scalability of semiconductor
quantum devices. Increasing device control comes at the cost of a large
parameter space that has to be explored in order to find the optimal operating
conditions. We demonstrate a statistical tuning algorithm that navigates this
entire parameter space, using just a few modelling assumptions, in the search
for specific electron transport features. We focused on gate-defined quantum
dot devices, demonstrating fully automated tuning of two different devices to
double quantum dot regimes in an up to eight-dimensional gate voltage space. We
considered a parameter space defined by the maximum range of each gate voltage
in these devices, demonstrating expected tuning in under 70 minutes. This
performance exceeded a human benchmark, although we recognise that there is
room for improvement in the performance of both humans and machines. Our
approach is approximately 180 times faster than a pure random search of the
parameter space, and it is readily applicable to different material systems and
device architectures. With an efficient navigation of the gate voltage space we
are able to give a quantitative measurement of device variability, from one
device to another and after a thermal cycle of a device. This is a key
demonstration of the use of machine learning techniques to explore and optimise
the parameter space of quantum devices and overcome the challenge of device
variability.
