Autotuning of Double-Dot Devices In Situ with Machine Learning

• DOI: 10.1103/physrevapplied.13.034075
• Publication Date: 2020-03-31T16:55:27Z
• Authors: 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; Jacob M. Taylor
• Keywords: Quantum Physics; 0103 physical sciences; FOS: Physical sciences; 02 engineering and technology; Quantum Physics (quant-ph); 0210 nano-technology; 01 natural sciences

9 pages, 7 figures<br/>The current practice of manually tuning quantum dots (QDs) for qubit operation is a relatively time-consuming procedure that is inherently impractical for scaling up and applications. In this work, we report on the {\it in situ} implementation of a recently proposed autotuning 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 a double-QD device can be used to replace human heuristics in the 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 the initial conditions and the device performance. Modifications to the training network, fitness function, and optimizer are discussed as a path toward further improvement in the success rate when starting both near and far detuned from the target double-dot range.<br/>