{"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/netket-3-machine-learning-toolbox-for-many","title":"NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems","arxiv_id":"2112.10526","date":"2021-12-20","proceeding":null,"authors":["Filippo Vicentini","Damian Hofmann","Attila Szabó","Dian Wu","Christopher Roth","Clemens Giuliani","Gabriel Pescia","Jannes Nys","Vladimir Vargas-Calderon","Nikita Astrakhantsev","Giuseppe Carleo"],"abstract":"We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. 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