This study presents a machine-learning-based procedure to automate the charge tuning of semiconductor spin qubits with minimal human intervention, addressing one of the significant challenges in scaling up quantum dot technologies. This method exploits artificial neural networks to identify noisy transition lines in stability diagrams, guiding a robust exploration strategy leveraging neural networks' uncertainty estimations. Tested across three distinct offline experimental datasets representing different single quantum dot technologies, the approach achieves over 99% tuning success rate in optimal cases, where more than 10% of the success is directly attributable to uncertainty exploitation. The challenging constraints of small training sets containing high diagram-to-diagram variability allowed us to evaluate the capabilities and limits of the proposed procedure.
@article{arxiv.2406.05175,
title = {Robust quantum dots charge autotuning using neural network uncertainty},
author = {Victor Yon and Bastien Galaup and Claude Rohrbacher and Joffrey Rivard and Clément Godfrin and Ruoyu Li and Stefan Kubicek and Kristiaan De Greve and Louis Gaudreau and Eva Dupont-Ferrier and Yann Beilliard and Roger G. Melko and Dominique Drouin},
journal= {arXiv preprint arXiv:2406.05175},
year = {2025}
}