Experimental Online Quantum Dots Charge Autotuning Using Neural Networks Article Swipe
Victor Yon
,
Bastien Galaup
,
Claude Rohrbacher
,
Joffrey Rivard
,
Alexis Morel
,
Dominic Leclerc
,
Clément Godfrin
,
RUYUE LI
,
Stefan Kubicek
,
Kristiaan De Greve
,
Emmanuel Ferrier
,
Yann Beilliard
,
Roger G. Melko
,
Dominique Drouin
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.nanolett.4c04889
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1021/acs.nanolett.4c04889
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 h and 9 min, 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.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1021/acs.nanolett.4c04889
- https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.4c04889?ref=article_openPDF
- OA Status
- bronze
- References
- 95
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408014918
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4408014918Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1021/acs.nanolett.4c04889Digital Object Identifier
- Title
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Experimental Online Quantum Dots Charge Autotuning Using Neural NetworksWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
-
2025-02-27Full publication date if available
- Authors
-
Victor Yon, Bastien Galaup, Claude Rohrbacher, Joffrey Rivard, Alexis Morel, Dominic Leclerc, Clément Godfrin, RUYUE LI, Stefan Kubicek, Kristiaan De Greve, Emmanuel Ferrier, Yann Beilliard, Roger G. Melko, Dominique DrouinList of authors in order
- Landing page
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https://doi.org/10.1021/acs.nanolett.4c04889Publisher landing page
- PDF URL
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https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.4c04889?ref=article_openPDFDirect link to full text PDF
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-
YesWhether a free full text is available
- OA status
-
bronzeOpen access status per OpenAlex
- OA URL
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https://pubs.acs.org/doi/pdf/10.1021/acs.nanolett.4c04889?ref=article_openPDFDirect OA link when available
- Concepts
-
Quantum dot, Charge (physics), Artificial neural network, Nanotechnology, Materials science, Quantum, Optoelectronics, Computer science, Physics, Artificial intelligence, Quantum mechanicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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95Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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