1. Experimental Online Quantum Dots Charge Autotuning Using Neural Network
- Author
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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., and Drouin, Dominique
- Subjects
Condensed Matter - Mesoscale and Nanoscale Physics ,Quantum Physics ,81V65 (Primary), 68T37 (Secondary) ,I.2.8 ,I.5.1 - Abstract
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. In 20 experimental runs on a device cooled to 25mK, the method achieved a success rate of 95% in locating the target electron regime, highlighting the robustness of this method 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., Comment: 6 pages (main) + 5 pages (supplementary)
- Published
- 2024