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: | Journal article |
| Language: | English |
| Published: |
Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften
2023
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| _version_ | 1811140330544693248 |
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| author | Schuff, J Lennon, DT Geyer, S Craig, DL Fedele, F Vigneau, F Camenzind, LC Kuhlmann, AV Briggs, GAD Zumbühl, DM Sejdinovic, D Ares, N |
| author_facet | Schuff, J Lennon, DT Geyer, S Craig, DL Fedele, F Vigneau, F Camenzind, LC Kuhlmann, AV Briggs, GAD Zumbühl, DM Sejdinovic, D Ares, N |
| author_sort | Schuff, J |
| collection | OXFORD |
| 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 | 2024-09-25T04:20:16Z |
| format | Journal article |
| id | oxford-uuid:438ed3f1-d456-4d46-8733-c242048ee9d3 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-09-25T04:20:16Z |
| publishDate | 2023 |
| publisher | Verein zur Forderung des Open Access Publizierens in den Quantenwissenschaften |
| record_format | dspace |
| spelling | oxford-uuid:438ed3f1-d456-4d46-8733-c242048ee9d32024-08-07T16:24:58ZIdentifying Pauli spin blockade using deep learningJournal articlehttp://purl.org/coar/resource_type/c_dcae04bcuuid:438ed3f1-d456-4d46-8733-c242048ee9d3EnglishSymplectic ElementsVerein zur Forderung des Open Access Publizierens in den Quantenwissenschaften2023Schuff, JLennon, DTGeyer, SCraig, DLFedele, FVigneau, FCamenzind, LCKuhlmann, AVBriggs, GADZumbühl, DMSejdinovic, DAres, NPauli 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. |
| spellingShingle | Schuff, J Lennon, DT Geyer, S Craig, DL Fedele, F Vigneau, F Camenzind, LC Kuhlmann, AV Briggs, GAD Zumbühl, DM Sejdinovic, D Ares, N Identifying Pauli spin blockade using deep learning |
| 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 |
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