Identifying Pauli spin blockade using deep learning
This provides a robust method for identifying PSB across quantum dot devices, aiding in spin qubit initialization and readout, though it is incremental as it applies existing deep learning techniques to a specific domain problem.
The authors tackled the problem of automatically identifying Pauli spin blockade (PSB) in quantum devices, which is challenging but useful for spin qubit operations, and achieved a 96% accuracy on test devices using a deep learning algorithm trained on simulated data.
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.