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Autotuning of double-dot devicesin situwith machine learning
Physical Review Applied ( IF 4.532 ) Pub Date : 
Justyna P. Zwolak, Thomas McJunkin, Sandesh S. Kalantre, J. P. Dodson, E. R. MacQuarrie, D. E. Savage, M. G. Lagally, S. N. Coppersmith, Mark A. Eriksson, and Jacob M. Taylor

The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure inherently impractical for scaling up and applications. In this work, we report on the {} implementation of a recently proposed auto-tuning protocol that combines machine learning (ML) with an optimization routine to navigate the parameter space. In particular, we show that a ML algorithm trained using exclusively simulated data to quantitatively classify the state of double QD device can be used to replace human heuristics in tuning of gate voltages in real devices. We demonstrate active feedback of a functional double dot device operated at millikelvin temperatures and discuss success rates as a function of initial conditions and device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path towards further improvement in the success rate when starting both near and far detuned from the target double dot range.
更新日期:2020-01-16

 

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