  Recent progress has shown that the dramatically increased number of
parameters has become a major issue in tuning of multi-quantum dot devices. The
complicated interactions between quantum dots and gate electrodes cause the
manual tuning process to no longer be efficient. Fortunately, machine learning
techniques can automate and speed up the tuning of simple quantum dot systems.
In this letter, we extend the techniques to tune multi-dot devices. We propose
an automated approach that combines machine learning, virtual gates and a
local-to-global method to realize the consecutive tuning of quantum dot arrays
by dividing them into subsystems. After optimizing voltage configurations and
establishing virtual gates to control each subsystem independently, a quantum
dot array can be efficiently tuned to the few-electron regime with appropriate
interdot tunnel coupling strength. Our experimental results show that this
approach can consecutively tune quantum dot arrays into an appropriate voltage
range without human intervention and possesses broad application prospects in
large-scale quantum dot devices.
