{"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/modern-applications-of-machine-learning-in","title":"Modern applications of machine learning in quantum sciences","arxiv_id":"2204.04198","date":"2022-04-08","proceeding":null,"authors":["Anna Dawid","Julian Arnold","Borja Requena","Alexander Gresch","Marcin Płodzień","Kaelan Donatella","Kim A. Nicoli","Paolo Stornati","Rouven Koch","Miriam Büttner","Robert Okuła","Gorka Muñoz-Gil","Rodrigo A. Vargas-Hernández","Alba Cervera-Lierta","Juan Carrasquilla","Vedran Dunjko","Marylou Gabrié","Patrick Huembeli","Evert van Nieuwenburg","Filippo Vicentini","Lei Wang","Sebastian J. Wetzel","Giuseppe Carleo","Eliška Greplová","Roman Krems","Florian Marquardt","Michał Tomza","Maciej Lewenstein","Alexandre Dauphin"],"abstract":"In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.","url_abs":"https://arxiv.org/abs/2204.04198v4","url_pdf":"https://arxiv.org/pdf/2204.04198v4.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":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"modern-applications-of-machine-learning-in","repo_url":"https://github.com/shmoo137/lecture-notes","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2204.04198","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}