Identifying Pauli spin blockade using deep learning - Schuff, J; Lennon, DT; Geyer, S; Craig, DL; Fedele, F; Vigneau, F; Camenzind, LC; Kuhlmann, AV; Briggs, GAD; Zumbühl, DM; Sejdinovic, D; Ares, N (Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften, 2023)
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. Our algorithm, an essential step for realising fully automatic qubit tuning, is expected to be employable across all types of quantum dot devices.