Robust quantum dots charge autotuning using neural network uncertainty

Abstract 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 network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% 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.

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Publication Details

Journal
Machine Learning Science and Technology
Published
2024-10-18
DOI
https://doi.org/10.1088/2632-2153/ad88d5
Citations
2
Primary Topic
Neural Networks and Applications
Type
article
Field-Weighted Citation Impact
0.69

Funders

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article

Robust quantum dots charge autotuning using neural network uncertainty

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Robust quantum dots charge autotuning using neural network uncertainty

Clément Godfrin, Victor Yon, Yann Beilliard, Claude Rohrbacher, Louis Gaudreau, Kristiaan De Greve, Eva Dupont-Ferrier, Joffrey Rivard, Dominique Drouin, Stefan Kubicek, Roger G. Melko, Bastien Galaup, Ruoyu Li
article en
2 citations

Abstract

Abstract 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 network uncertainty estimations. Tested across three distinct offline experimental datasets representing different single-quantum-dot technologies, this approach achieves a tuning success rate of over 99% 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.

Machine Learning Science and TechnologyVol. 5(4)
Université de Sherbrooke (CA), University of Waterloo (CA), Perimeter Institute (CA), IMEC (BE), National Research Council Canada (CA)
Innovation, Science and Economic Development Canada, Government of Canada, Ministero dello Sviluppo Economico, Institut Périmètre de physique théorique, Natural Sciences and Engineering Research Council of Canada, Fonds de recherche du Québec – Nature et technologies
Openalex Percentile: Top 24%
Neural Networks and Applications
0.69
DOIOpen Access PDFOpenAlex
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