Experimental online quantum dots charge autotuning using neural networks

• URL: http://arxiv.org/abs/2409.20320v2
• Date: Tue, 11 Feb 2025 21:02:07 GMT
• Title: Experimental online quantum dots charge autotuning using neural networks
• Authors: Victor Yon, Bastien Galaup, Claude Rohrbacher, Joffrey Rivard, Alexis Morel, Dominic Leclerc, Clement Godfrin, Ruoyu Li, Stefan Kubicek, Kristiaan De Greve, Eva Dupont-Ferrier, Yann Beilliard, Roger G. Melko, Dominique Drouin,
• Abstract summary: Experimental demonstration of online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. In 20 experimental runs, our method achieved a success rate of 95% in locating the target electron regime. This work validates the feasibility of machine learning-driven real-time charge autotuning for quantum dot devices.
• Score: 0.8219694762753349
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• 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. 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.

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