1Department of Materials, University of Oxford, Oxford OX1 3PH, United Kingdom
2Department of Physics, University of Basel, 4056 Basel, Switzerland
3School of Computer and Mathematical Sciences & AIML, University of Adelaide, SA 5005, Australia
4Department of Engineering Science, University of Oxford, Oxford OX1 3PJ, United Kingdom
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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. Our algorithm, an essential step for realising fully automatic qubit tuning, is expected to be employable across all types of quantum dot devices.
Featured image: Illustration of a Pauli spin blockade detector
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Cited by
[1] Ludmila Szulakowska and Jun Dai, “Bayesian autotuning of Hubbard model quantum simulators”, arXiv:2210.03077, (2022).
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