{"main_file_link":[{"open_access":"1","url":"https://doi.org/10.48550/arXiv.2107.12975"}],"acknowledgement":"We acknowledge Ang Li, Erik P. A. M. Bakkers (University of Eindhoven) for the fabrication of the Ge/Si nanowire. This work was supported by the Royal Society, the EPSRC National Quantum Technology Hub in Networked Quantum Information Technology (EP/M013243/1), Quantum Technology Capital (EP/N014995/1), EPSRC Platform Grant\r\n(EP/R029229/1), the European Research Council (Grant agreement 948932), the Swiss Nanoscience Institute, the\r\nNCCR SPIN, the EU H2020 European Microkelvin Platform EMP grant No. 824109, the Scientific Service Units\r\nof IST Austria through resources provided by the nanofabrication facility and, the FWF-P30207 project. This publication was also made possible through support from Templeton World Charity Foundation and John Templeton Foundation. The opinions expressed in this publication are those of the authors and do not necessarily reflect the views of the Templeton Foundations.","publication":"arXiv","project":[{"name":"Hole spin orbit qubits in Ge quantum wells","_id":"2641CE5E-B435-11E9-9278-68D0E5697425","call_identifier":"FWF","grant_number":"P30207"}],"year":"2021","oa_version":"Preprint","citation":{"chicago":"Severin, B., D. T. Lennon, L. C. Camenzind, F. Vigneau, F. Fedele, Daniel Jirovec, A. Ballabio, et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” <i>ArXiv</i>, n.d. <a href=\"https://doi.org/10.48550/arXiv.2107.12975\">https://doi.org/10.48550/arXiv.2107.12975</a>.","short":"B. Severin, D.T. Lennon, L.C. Camenzind, F. Vigneau, F. Fedele, D. Jirovec, A. Ballabio, D. Chrastina, G. Isella, M. de Kruijf, M.J. Carballido, S. Svab, A.V. Kuhlmann, F.R. Braakman, S. Geyer, F.N.M. Froning, H. Moon, M.A. Osborne, D. Sejdinovic, G. Katsaros, D.M. Zumbühl, G.A.D. Briggs, N. Ares, ArXiv (n.d.).","apa":"Severin, B., Lennon, D. T., Camenzind, L. C., Vigneau, F., Fedele, F., Jirovec, D., … Ares, N. (n.d.). Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. <i>arXiv</i>. <a href=\"https://doi.org/10.48550/arXiv.2107.12975\">https://doi.org/10.48550/arXiv.2107.12975</a>","ista":"Severin B, Lennon DT, Camenzind LC, Vigneau F, Fedele F, Jirovec D, Ballabio A, Chrastina D, Isella G, Kruijf M de, Carballido MJ, Svab S, Kuhlmann AV, Braakman FR, Geyer S, Froning FNM, Moon H, Osborne MA, Sejdinovic D, Katsaros G, Zumbühl DM, Briggs GAD, Ares N. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. arXiv, 2107.12975.","ama":"Severin B, Lennon DT, Camenzind LC, et al. Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning. <i>arXiv</i>. doi:<a href=\"https://doi.org/10.48550/arXiv.2107.12975\">10.48550/arXiv.2107.12975</a>","ieee":"B. Severin <i>et al.</i>, “Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning,” <i>arXiv</i>. .","mla":"Severin, B., et al. “Cross-Architecture Tuning of Silicon and SiGe-Based Quantum Devices Using Machine Learning.” <i>ArXiv</i>, 2107.12975, doi:<a href=\"https://doi.org/10.48550/arXiv.2107.12975\">10.48550/arXiv.2107.12975</a>."},"status":"public","related_material":{"record":[{"id":"10058","status":"public","relation":"dissertation_contains"}]},"type":"preprint","department":[{"_id":"GeKa"}],"article_number":"2107.12975","month":"07","title":"Cross-architecture tuning of silicon and SiGe-based quantum devices using machine learning","doi":"10.48550/arXiv.2107.12975","acknowledged_ssus":[{"_id":"NanoFab"}],"article_processing_charge":"No","publication_status":"submitted","author":[{"first_name":"B.","full_name":"Severin, B.","last_name":"Severin"},{"last_name":"Lennon","full_name":"Lennon, D. T.","first_name":"D. T."},{"full_name":"Camenzind, L. C.","first_name":"L. C.","last_name":"Camenzind"},{"last_name":"Vigneau","first_name":"F.","full_name":"Vigneau, F."},{"first_name":"F.","full_name":"Fedele, F.","last_name":"Fedele"},{"last_name":"Jirovec","orcid":"0000-0002-7197-4801","id":"4C473F58-F248-11E8-B48F-1D18A9856A87","first_name":"Daniel","full_name":"Jirovec, Daniel"},{"last_name":"Ballabio","first_name":"A.","full_name":"Ballabio, A."},{"last_name":"Chrastina","full_name":"Chrastina, D.","first_name":"D."},{"full_name":"Isella, G.","first_name":"G.","last_name":"Isella"},{"last_name":"Kruijf","first_name":"M. de","full_name":"Kruijf, M. de"},{"first_name":"M. J.","full_name":"Carballido, M. J.","last_name":"Carballido"},{"last_name":"Svab","full_name":"Svab, S.","first_name":"S."},{"last_name":"Kuhlmann","first_name":"A. V.","full_name":"Kuhlmann, A. V."},{"first_name":"F. R.","full_name":"Braakman, F. R.","last_name":"Braakman"},{"last_name":"Geyer","full_name":"Geyer, S.","first_name":"S."},{"full_name":"Froning, F. N. M.","first_name":"F. N. M.","last_name":"Froning"},{"first_name":"H.","full_name":"Moon, H.","last_name":"Moon"},{"full_name":"Osborne, M. A.","first_name":"M. A.","last_name":"Osborne"},{"last_name":"Sejdinovic","full_name":"Sejdinovic, D.","first_name":"D."},{"orcid":"0000-0001-8342-202X","last_name":"Katsaros","id":"38DB5788-F248-11E8-B48F-1D18A9856A87","first_name":"Georgios","full_name":"Katsaros, Georgios"},{"last_name":"Zumbühl","full_name":"Zumbühl, D. M.","first_name":"D. M."},{"last_name":"Briggs","first_name":"G. A. D.","full_name":"Briggs, G. A. D."},{"last_name":"Ares","full_name":"Ares, N.","first_name":"N."}],"day":"27","date_updated":"2024-04-23T22:30:25Z","_id":"10066","abstract":[{"text":"The potential of Si and SiGe-based devices for the scaling of quantum circuits is tainted by device variability. Each device needs to be tuned to operation conditions. We give a key step towards tackling this variability with an algorithm that, without modification, is capable of tuning a 4-gate Si FinFET, a 5-gate GeSi nanowire and a 7-gate SiGe heterostructure double quantum dot device from scratch. We achieve tuning times of 30, 10, and 92 minutes, respectively. The algorithm also provides insight into the parameter space landscape for each of these devices. These results show that overarching solutions for the tuning of quantum devices are enabled by machine learning.","lang":"eng"}],"oa":1,"date_created":"2021-10-01T12:40:22Z","user_id":"2DF688A6-F248-11E8-B48F-1D18A9856A87","date_published":"2021-07-27T00:00:00Z","language":[{"iso":"eng"}],"external_id":{"arxiv":["2107.12975"]}}