Robust quantum dots charge autotuning using neural network uncertainty

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 tr...

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Veröffentlicht in:	Machine learning: science and technology 2024-12, Vol.5 (4), p.45034
Hauptverfasser:	Yon, Victor, Galaup, Bastien, Rohrbacher, Claude, Rivard, Joffrey, Godfrin, Clément, Li, Ruoyu, Kubicek, Stefan, De Greve, Kristiaan, Gaudreau, Louis, Dupont-Ferrier, Eva, Beilliard, Yann, Melko, Roger G, Drouin, Dominique
Format:	Artikel
Sprache:	eng
Schlagworte:	artificial neural network Artificial neural networks Bayesian neural network charge autotuning Machine learning quantum dot Quantum dots Qubits (quantum computing) Robustness Tuning Uncertainty uncertainty estimation
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