Visual explanations of machine learning model estimating charge states in quantum dots

Autor: Yui Muto, Takumi Nakaso, Motoya Shinozaki, Takumi Aizawa, Takahito Kitada, Takashi Nakajima, Matthieu R. Delbecq, Jun Yoneda, Kenta Takeda, Akito Noiri, Arne Ludwig, Andreas D. Wieck, Seigo Tarucha, Atsunori Kanemura, Motoki Shiga, Tomohiro Otsuka
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: APL Machine Learning, Vol 2, Iss 2, Pp 026110-026110-7 (2024)
Druh dokumentu: article
ISSN: 2770-9019
DOI: 10.1063/5.0193621
Popis: 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. This technique highlights the important regions in the image for predicting the class. 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.
Databáze: Directory of Open Access Journals
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