Identifying Pauli spin blockade using deep learning

TL;DR

This paper introduces a deep learning method that automatically detects Pauli spin blockade in quantum devices using charge transport data, achieving high accuracy and robustness across different devices.

Contribution

The paper presents a novel machine learning algorithm trained on simulated data to identify Pauli spin blockade, addressing data scarcity and demonstrating cross-device robustness.

Findings

96% accuracy on test devices

Robust detection across device variability

Applicable to various quantum dot devices

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.