Noise robust automatic charge state recognition in quantum dots by
machine learning and pre-processing, and visual explanations of the model
with Grad-CAM
Charge state recognition in quantum dot devices is important in preparation
of quantum bits for quantum information processing. Towards auto-tuning of
larger-scale quantum devices, automatic charge state recognition by machine
learning has been demonstrated. In this work, we propose a simpler method using
machine learning and pre-processing. We demonstrate the operation of the charge
state recognition and evaluated an accuracy high as 96%. We also analyze the
explainability of the trained machine learning model by gradient-weighted class
activation mapping (Grad-CAM) which identifies class-discriminative regions for
the predictions. It exhibits that the model predicts the state based on the
change transition lines, indicating human-like recognition is realized.Comment: 15 pages, 6 figure