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...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften
2023-08-01
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| Series: | Quantum |
| Online Access: | https://quantum-journal.org/papers/q-2023-08-08-1077/pdf/ |
| _version_ | 1797750792908701696 |
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| author | Jonas Schuff Dominic T. Lennon Simon Geyer David L. Craig Federico Fedele Florian Vigneau Leon C. Camenzind Andreas V. Kuhlmann G. Andrew D. Briggs Dominik M. Zumbühl Dino Sejdinovic Natalia Ares |
| author_facet | Jonas Schuff Dominic T. Lennon Simon Geyer David L. Craig Federico Fedele Florian Vigneau Leon C. Camenzind Andreas V. Kuhlmann G. Andrew D. Briggs Dominik M. Zumbühl Dino Sejdinovic Natalia Ares |
| author_sort | Jonas Schuff |
| collection | DOAJ |
| 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. Our algorithm, an essential step for realising fully automatic qubit tuning, is expected to be employable across all types of quantum dot devices. |
| first_indexed | 2024-03-12T16:37:57Z |
| format | Article |
| id | doaj.art-e27c7deb5e84463a90719c9054a5fd7b |
| institution | Directory Open Access Journal |
| issn | 2521-327X |
| language | English |
| last_indexed | 2024-03-12T16:37:57Z |
| publishDate | 2023-08-01 |
| publisher | Verein zur Förderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | Article |
| series | Quantum |
| spelling | doaj.art-e27c7deb5e84463a90719c9054a5fd7b2023-08-08T14:48:52ZengVerein zur Förderung des Open Access Publizierens in den QuantenwissenschaftenQuantum2521-327X2023-08-017107710.22331/q-2023-08-08-107710.22331/q-2023-08-08-1077Identifying Pauli spin blockade using deep learningJonas SchuffDominic T. LennonSimon GeyerDavid L. CraigFederico FedeleFlorian VigneauLeon C. CamenzindAndreas V. KuhlmannG. Andrew D. BriggsDominik M. ZumbühlDino SejdinovicNatalia AresPauli 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.https://quantum-journal.org/papers/q-2023-08-08-1077/pdf/ |
| spellingShingle | Jonas Schuff Dominic T. Lennon Simon Geyer David L. Craig Federico Fedele Florian Vigneau Leon C. Camenzind Andreas V. Kuhlmann G. Andrew D. Briggs Dominik M. Zumbühl Dino Sejdinovic Natalia Ares Identifying Pauli spin blockade using deep learning Quantum |
| title | Identifying Pauli spin blockade using deep learning |
| title_full | Identifying Pauli spin blockade using deep learning |
| title_fullStr | Identifying Pauli spin blockade using deep learning |
| title_full_unstemmed | Identifying Pauli spin blockade using deep learning |
| title_short | Identifying Pauli spin blockade using deep learning |
| title_sort | identifying pauli spin blockade using deep learning |
| url | https://quantum-journal.org/papers/q-2023-08-08-1077/pdf/ |
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