  Automated tuning of gate-defined quantum dots is a requirement for large
scale semiconductor based qubit initialisation. An essential step of these
tuning procedures is charge state detection based on charge stability diagrams.
Using supervised machine learning to perform this task requires a large dataset
for models to train on. In order to avoid hand labelling experimental data,
synthetic data has been explored as an alternative. While providing a
significant increase in the size of the training dataset compared to using
experimental data, using synthetic data means that classifiers are trained on
data sourced from a different distribution than the experimental data that is
part of the tuning process. Here we evaluate the prediction accuracy of a range
of machine learning models trained on simulated and experimental data and their
ability to generalise to experimental charge stability diagrams in two
dimensional electron gas and nanowire devices. We find that classifiers perform
best on either purely experimental or a combination of synthetic and
experimental training data, and that adding common experimental noise
signatures to the synthetic data does not dramatically improve the
classification accuracy. These results suggest that experimental training data
as well as realistic quantum dot simulations and noise models are essential in
charge state detection using supervised machine learning.
