Experimental online quantum dots charge autotuning using neural networks
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912251395440640 |
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| author | Yon, Victor Galaup, Bastien Rohrbacher, Claude Rivard, Joffrey Morel, Alexis Leclerc, Dominic Godfrin, Clement Li, Ruoyu Kubicek, Stefan De Greve, Kristiaan Dupont-Ferrier, Eva Beilliard, Yann Melko, Roger G. Drouin, Dominique |
| author_facet | Yon, Victor Galaup, Bastien Rohrbacher, Claude Rivard, Joffrey Morel, Alexis Leclerc, Dominic Godfrin, Clement Li, Ruoyu Kubicek, Stefan De Greve, Kristiaan Dupont-Ferrier, Eva Beilliard, Yann Melko, Roger G. Drouin, Dominique |
| contents | Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. This exploration leverages the model's uncertainty estimation to find the appropriate gate configuration with minimal measurements while reducing the risk of failures. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this approach against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_20320 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Experimental online quantum dots charge autotuning using neural networks Yon, Victor Galaup, Bastien Rohrbacher, Claude Rivard, Joffrey Morel, Alexis Leclerc, Dominic Godfrin, Clement Li, Ruoyu Kubicek, Stefan De Greve, Kristiaan Dupont-Ferrier, Eva Beilliard, Yann Melko, Roger G. Drouin, Dominique Mesoscale and Nanoscale Physics Quantum Physics 81V65 (Primary), 68T37 (Secondary) I.2.8; I.5.1 Spin-based semiconductor qubits hold promise for scalable quantum computing, yet they require reliable autonomous calibration procedures. This study presents an experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. The autotuning algorithm explores the gates' voltage space to localize charge transition lines, thereby isolating the one-electron regime without human intervention. This exploration leverages the model's uncertainty estimation to find the appropriate gate configuration with minimal measurements while reducing the risk of failures. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this approach against noise and distribution shifts from the offline training set. Each tuning run lasted an average of 2 hours and 9 minutes, primarily due to the limited speed of the current measurement. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices, advancing the development toward the control of large qubit arrays. |
| title | Experimental online quantum dots charge autotuning using neural networks |
| topic | Mesoscale and Nanoscale Physics Quantum Physics 81V65 (Primary), 68T37 (Secondary) I.2.8; I.5.1 |
| url | https://arxiv.org/abs/2409.20320 |