Identifying Pauli spin blockade using deep learning
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 scarcit...
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| Veröffentlicht in: | arXiv.org 2023-08 |
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| creator | Schuff, Jonas Lennon, Dominic T Geyer, Simon Craig, David L Fedele, Federico Vigneau, Florian Camenzind, Leon C Kuhlmann, Andreas V Briggs, G Andrew D Zumbühl, Dominik M Sejdinovic, Dino Ares, Natalia |
| description | 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. |
| doi_str_mv | 10.48550/arxiv.2202.00574 |
| format | Article |
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| subjects | Algorithms Charge transport Computer Science - Learning Deep learning Field effect transistors High temperature Machine learning Physics - Mesoscale and Nanoscale Physics Physics - Quantum Physics Quantum dots Qubits (quantum computing) Semiconductor devices |
| title | Identifying Pauli spin blockade using deep learning |
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