{"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/efficiently-measuring-a-quantum-device-using","title":"Efficiently measuring a quantum device using machine learning","arxiv_id":"1810.10042","date":"2018-10-23","proceeding":null,"authors":["D. T. Lennon","H. Moon","L. C. Camenzind","Liuqi Yu","D. M. Zumbühl","G. A. D. Briggs","M. A. Osborne","E. A. Laird","N. Ares"],"abstract":"Scalable quantum technologies will present challenges for characterizing and\ntuning quantum devices. This is a time-consuming activity, and as the size of\nquantum systems increases, this task will become intractable without the aid of\nautomation. We present measurements on a quantum dot device performed by a\nmachine learning algorithm. The algorithm selects the most informative\nmeasurements to perform next using information theory and a probabilistic\ndeep-generative model, the latter capable of generating multiple\nfull-resolution reconstructions from scattered partial measurements. We\ndemonstrate, for two different measurement configurations, that the algorithm\noutperforms standard grid scan techniques, reducing the number of measurements\nrequired by up to 4 times and the measurement time by 3.7 times. Our\ncontribution goes beyond the use of machine learning for data search and\nanalysis, and instead presents the use of algorithms to automate measurement.\nThis work lays the foundation for automated control of large quantum circuits.","url_abs":"http://arxiv.org/abs/1810.10042v1","url_pdf":"http://arxiv.org/pdf/1810.10042v1.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":"efficiently-measuring-a-quantum-device-using","repo_url":"https://github.com/oxquantum-repo/CVAE_for_QE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"efficiently-measuring-a-quantum-device-using","repo_url":"https://github.com/oxquantum/CVAE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"efficiently-measuring-a-quantum-device-using","repo_url":"https://github.com/oxquantum/CVAE_for_QE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"efficiently-measuring-a-quantum-device-using","repo_url":"https://github.com/returnddd/CVAE_for_QE","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}