Visual explanations of machine learning model estimating charge states in quantum dots
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arXiv
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| Main Authors: | , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2022
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| Subjects: | |
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| _version_ | 1866910411322818560 |
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| author | Muto, Yui Nakaso, Takumi Shinozaki, Motoya Aizawa, Takumi Kitada, Takahito Nakajima, Takashi Delbecq, Matthieu R. Yoneda, Jun Takeda, Kenta Noiri, Akito Ludwig, Arne Wieck, Andreas D. Tarucha, Seigo Kanemura, Atsunori Shiga, Motoki Otsuka, Tomohiro |
| author_facet | Muto, Yui Nakaso, Takumi Shinozaki, Motoya Aizawa, Takumi Kitada, Takahito Nakajima, Takashi Delbecq, Matthieu R. Yoneda, Jun Takeda, Kenta Noiri, Akito Ludwig, Arne Wieck, Andreas D. Tarucha, Seigo Kanemura, Atsunori Shiga, Motoki Otsuka, Tomohiro |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2210_15070 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | Visual explanations of machine learning model estimating charge states in quantum dots Muto, Yui Nakaso, Takumi Shinozaki, Motoya Aizawa, Takumi Kitada, Takahito Nakajima, Takashi Delbecq, Matthieu R. Yoneda, Jun Takeda, Kenta Noiri, Akito Ludwig, Arne Wieck, Andreas D. Tarucha, Seigo Kanemura, Atsunori Shiga, Motoki Otsuka, Tomohiro Mesoscale and Nanoscale Physics 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. |
| title | Visual explanations of machine learning model estimating charge states in quantum dots |
| topic | Mesoscale and Nanoscale Physics |
| url | https://arxiv.org/abs/2210.15070 |