Experimental Online Quantum Dots Charge Autotuning Using Neural Networks

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 calibr...

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Bibliographic Details
Main Author:	Victor Yon (20797473) (author)
Other Authors:	Bastien Galaup (20797476) (author), Claude Rohrbacher (20797479) (author), Joffrey Rivard (20797482) (author), Alexis Morel (20797485) (author), Dominic Leclerc (20797488) (author), Clément Godfrin (3926795) (author), Ruoyu Li (74020) (author), Stefan Kubicek (172383) (author), Kristiaan De Greve (20797491) (author), Eva Dupont Ferrier (20797494) (author), Yann Beilliard (10736217) (author), Roger G. Melko (11920011) (author), Dominique Drouin (1623367) (author)
Published:	2025
Subjects:	Neuroscience Biotechnology Computational Biology Space Science Environmental Sciences not elsewhere classified Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Physical Sciences not elsewhere classified Information Systems not elsewhere classified tuning run lasted time charge autotuning target electron regime scalable quantum computing quantum dot devices offline training set loop calibration system large qubit arrays autotuning algorithm explores appropriate gate configuration 20 experimental runs experimental demonstration work validates uncertainty estimation thereby isolating success rate study presents primarily due online single model ’ minimal measurements method achieved limited speed exploration leverages driven real distribution shifts development toward current measurement 9 min 2 h
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Experimental Online Quantum Dots Charge Autotuning Using Neural Networks

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