  This study presents a machine-learning-based procedure to automate the charge
tuning of semiconductor spin qubits with minimal human intervention, addressing
one of the significant challenges in scaling up quantum dot technologies. This
method exploits artificial neural networks to identify noisy transition lines
in stability diagrams, guiding a robust exploration strategy leveraging neural
networks' uncertainty estimations. Tested across three distinct offline
experimental datasets representing different single quantum dot technologies,
the approach achieves over 99% tuning success rate in optimal cases, where more
than 10% of the success is directly attributable to uncertainty exploitation.
The challenging constraints of small training sets containing high
diagram-to-diagram variability allowed us to evaluate the capabilities and
limits of the proposed procedure.
