Experimental Online Quantum Dots Charge Autotuning Using Neural Network

• URL: http://arxiv.org/abs/2409.20320v1
• Date: Mon, 30 Sep 2024 14:22:47 GMT
• Title: Experimental Online Quantum Dots Charge Autotuning Using Neural Network
• 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: This study shows online single-dot charge autotuning using a convolutional neural network integrated into a closed-loop calibration system. In 20 experimental runs on a device cooled to 25mK, the 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
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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