  Defining quantum dots in semiconductor based heterostructures is an essential
step in initializing solid-state qubits. With growing device complexity and
increasing number of functional devices required for measurements, a manual
approach to finding suitable gate voltages to confine electrons
electrostatically is impractical. Here, we implement a two-stage device
characterization and dot-tuning process which first determines whether devices
are functional and then attempts to tune the functional devices to the single
or double quantum dot regime. We show that automating well established manual
tuning procedures and replacing the experimenter's decisions by supervised
machine learning is sufficient to tune double quantum dots in multiple devices
without pre-measured input or manual intervention. The quality of measurement
results and charge states are assessed by four binary classifiers trained with
experimental data, reflecting real device behaviour. We compare and optimize
eight models and different data preprocessing techniques for each of the
classifiers to achieve reliable autonomous tuning, an essential step towards
scalable quantum systems in quantum dot based qubit architectures.
