  The current practice of manually tuning quantum dots (QDs) for qubit
operation is a relatively time-consuming procedure that is inherently
impractical for scaling up and applications. In this work, we report on the
{\it in situ} implementation of a recently proposed autotuning protocol that
combines machine learning (ML) with an optimization routine to navigate the
parameter space. In particular, we show that a ML algorithm trained using
exclusively simulated data to quantitatively classify the state of a double-QD
device can be used to replace human heuristics in the tuning of gate voltages
in real devices. We demonstrate active feedback of a functional double-dot
device operated at millikelvin temperatures and discuss success rates as a
function of the initial conditions and the device performance. Modifications to
the training network, fitness function, and optimizer are discussed as a path
toward further improvement in the success rate when starting both near and far
detuned from the target double-dot range.
