Published in Quantum: Identifying Pauli spin blockade using deep learning

PSB detector

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. In this paper, 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.

Collaboration between the Ares group (Oxford University) and Zumbühl group (University of Basel), also supported by NCCR SPIN of the Swiss NSF.

Identifying Pauli spin blockade using deep learning
Quantum 7, 1077 (Aug 8, 2023), manuscript pdf