Identifying Pauli spin blockade using deep learning with scarce experimental data

A method to readout spin qubits encoded in quantum dot devices relies on Pauli spin blockade (PSB) for spin-to-charge conversion. PSB leads to transport features that are hard to detect even for human experts. We present a machine learning algorithm capable of automatically identifying PSB. The scarcity of PSB data is circumvented by training the algorithm with simulated data. We demonstrate our approach on a silicon fin field-effect transistor device and report an accuracy of 96% on different test devices, giving proof that the approach is robust to device variability.