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.

Bayesian autotuning of Hubbard model quantum simulators

“…Machine learning (ML) has emerged as a promising tool for some of the experimental challenges with QD control [37]. Deep neural networks [39][40][41][42][43][44][45][46], image recognition [38,40,42,[47][48][49][50] and supervised classification [40,46,51] have been demonstrated to aid charge state characterization [41,42,48,49,51], coupling parameter tuning [37] and gate voltage optimization [36,41,43,46,49,52] in a single QD [43,49,51], double QDs [36-38, 42-44, 51], triple QDs and arrays of QDs [36,37,41,45,48]. Unsupervised statistical methods [52,53] and deterministic algorithms [36,[49][50][51] have also been used for double-QD tuning.…”