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.

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Cited by in corpus (5)

• Artificial Intelligence for Quantum Computing
• Data needs and challenges for quantum dot devices automation
• Cross-architecture Tuning of Silicon and SiGe-based Quantum Devices Using Machine Learning
• Fully autonomous tuning of a spin qubit
• Rapid Autotuning of a SiGe Quantum Dot into the Single-Electron Regime with Machine Learning and RF-Reflectometry FPGA-Based Measurements