{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/identifying-pauli-spin-blockade-using-deep","title":"Identifying Pauli spin blockade using deep learning","arxiv_id":"2202.00574","date":"2022-02-01","proceeding":null,"authors":["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"],"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.","url_abs":"https://arxiv.org/abs/2202.00574v4","url_pdf":"https://arxiv.org/pdf/2202.00574v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"identifying-pauli-spin-blockade-using-deep","repo_url":"https://github.com/oxquantum-repo/identifying-psb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"deep-learning","task_name":"Deep Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}