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 is 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 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.

Spherically-bent crystal analyzers (SBCAs) see considerable use in very high-resolution hard Xray wavelength dispersive X-ray fluorescence spectroscopy, often called X-ray emission spectroscopy (XES). While Si and Ge are the most frequently used diffractive components of SBCAs, we consider here the somewhat classical choice of muscovite mica as the dispersing element. We find that the various harmonics of a highest-quality mica-based SBCA show ~5% to -~40% of the integral reflectivity per unit solid angle of a typical Si or Ge SBCA in the hard Xray range, and that the mica SBCA have comparable energy resolution to the traditional SBCAs.