Identifying Pauli spin blockade using deep learning

Abstract

Pauli spin blockade (PSB) can be employed as a great resource for spin qubit initialisation and readout even at elevated temperatures but it can be difficult to identify. We present a machine learning algorithm capable of automatically identifying PSB using charge transport measurements. The scarcity of PSB data is circumvented by training the algorithm with simulated data and by using cross-device validation. We demonstrate our approach on a silicon field-effect transistor device and report an accuracy of 96% on different test devices, giving evidence that the approach is robust to device variability. The approach is expected to be employable across all types of quantum dot devices.

Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning

“…The time-consuming challenge of tuning semiconductor devices becomes intractable as we combine different device architectures in the realisation of complex quantum circuits with millions of components. The development of machine learning algorithms for quantum device tuning [10][11][12][13][14][15][16][17][18][19][20][21][22][23][24][25] is exceptionally challenging when looking for such overarching solutions, successful on very different types of devices which may need to cater to specific purposes.…”