  Charge state recognition in quantum dot devices is important in the
preparation of quantum bits for quantum information processing. Toward
auto-tuning of larger-scale quantum devices, automatic charge state recognition
by machine learning has been demonstrated. For further development of this
technology, an understanding of the operation of the machine learning model,
which is usually a black box, will be useful. In this study, we analyze the
explainability of the machine learning model estimating charge states in
quantum dots by gradient-weighted class activation mapping, which identified
class-discriminative regions for the predictions. The model predicts the state
based on the change transition lines, indicating that human-like recognition is
realized. We also demonstrate improvements of the model by utilizing feedback
from the mapping results. Due to the simplicity of our simulation and
pre-processing methods, our approach offers scalability without significant
additional simulation costs, demonstrating its suitability for future quantum
dot system expansions.
