  The current autotuning approaches for quantum dot (QD) devices, while showing
some success, lack an assessment of data reliability. This leads to unexpected
failures when noisy or otherwise low-quality data is processed by an autonomous
system. In this work, we propose a framework for robust autotuning of QD
devices that combines a machine learning (ML) state classifier with a data
quality control module. The data quality control module acts as a "gatekeeper"
system, ensuring that only reliable data are processed by the state classifier.
Lower data quality results in either device recalibration or termination. To
train both ML systems, we enhance the QD simulation by incorporating synthetic
noise typical of QD experiments. We confirm that the inclusion of synthetic
noise in the training of the state classifier significantly improves the
performance, resulting in an accuracy of 95.0(9) % when tested on experimental
data. We then validate the functionality of the data quality control module by
showing that the state classifier performance deteriorates with decreasing data
quality, as expected. Our results establish a robust and flexible ML framework
for autonomous tuning of noisy QD devices.
