{"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-a-machine-learning-toolkit-for-many","title":"NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems","arxiv_id":"1904.00031","date":"2019-03-29","proceeding":null,"authors":["Giuseppe Carleo","Kenny Choo","Damian Hofmann","James E. T. Smith","Tom Westerhout","Fabien Alet","Emily J. Davis","Stavros Efthymiou","Ivan Glasser","Sheng-Hsuan Lin","Marta Mauri","Guglielmo Mazzola","Christian B. Mendl","Evert van Nieuwenburg","Ossian O'Reilly","Hugo Théveniaut","Giacomo Torlai","Alexander Wietek"],"abstract":"We introduce NetKet, a comprehensive open source framework for the study of many-body quantum systems using machine learning techniques. The framework is built around a general and flexible implementation of neural-network quantum states, which are used as a variational ansatz for quantum wave functions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wave-function data, and ground state searches for a wide range of customizable lattice models. Our aim is to provide a common platform for open research and to stimulate the collaborative development of computational methods at the interface of machine learning and many-body physics.","url_abs":"https://arxiv.org/abs/1904.00031v1","url_pdf":"https://arxiv.org/pdf/1904.00031v1.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":"netket-a-machine-learning-toolkit-for-many","repo_url":"https://github.com/netket/netket","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"jax","reach":{"status":"ok","spdx":"Apache-2.0"}}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1904.00031","atlas_url":"https://app.syntology.ai/?focus=1904.00031","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}