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NetKet: A machine learning toolkit for many-body quantum systems

Carleo, Giuseppe and Choo, Kenny and Hofmann, Damian and Smith, James E. T. and Westerhout, Tom and Alet, Fabien and Davis, Emily J. and Efthymiou, Stavros and Glasser, Ivan and Lin, Sheng-Hsuan and Mauri, Marta and Mazzola, Guglielmo and Mendl, Christian B. and van Nieuwenburg, Evert and O’Reilly, Ossian and Théveniaut, Hugo and Torlai, Giacomo and Vicentini, Filippo and Wietek, Alexander (2019) NetKet: A machine learning toolkit for many-body quantum systems. SoftwareX, 10 . Art. No. 100311. ISSN 2352-7110. https://resolver.caltech.edu/CaltechAUTHORS:20190827-093549272

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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 wavefunctions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wavefunction 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.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.softx.2019.100311DOIArticle
ORCID:
AuthorORCID
van Nieuwenburg, Evert0000-0003-0323-0031
Torlai, Giacomo0000-0001-8478-4436
Additional Information:© 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Received 28 March 2019, Revised 9 August 2019, Accepted 12 August 2019, Available online 27 August 2019. We acknowledge support from the Flatiron Institute of the Simons Foundation. J.E.T.S. gratefully acknowledges support from a fellowship through The Molecular Sciences Software Institute under NSF Grant ACI1547580. H.T. is supported by a grant from the Fondation CFM pour la Recherche. S.E. and I.G. are supported by an ERC Advanced Grant QENOCOBA under the EU Horizon2020 program (grant agreement 742102) and the German Research Foundation (DFG) under Germany’s Excellence Strategy through Project No. EXC-2111 -390814868 (MCQST). This project makes use of other open source software, namely pybind11 [16], Eigen [19], nlohmann/json [21], NumPy [20], and SciPy [18]. Pre-release versions of NetKet 2.0 have used a Lanczos solver based on the IETL library from the ALPS project [52], [53], which implements a variant of the Lanczos algorithm due to Cullum and Willoughby [54], [55]. We further acknowledge discussions with, as well as bug reports, comments, and support from S. Arnold, A. Booth, A. Borin, J. Carrasquilla, C. Ciuti, S. Lederer, Y. Levine, T. Neupert, O. Parcollet, A. Rubio, M. A. Sentef, O. Sharir, M. Stoudenmire, and N. Wies. The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Group:IQIM, Institute for Quantum Information and Matter
Funders:
Funding AgencyGrant Number
Flatiron InstituteUNSPECIFIED
Simons FoundationUNSPECIFIED
NSFACI-1547580
Fondation CFM pour la RechercheUNSPECIFIED
European Research Council (ERC)742102
Deutsche Forschungsgemeinschaft (DFG)EXC-2111-390814868
Subject Keywords:Neural-network quantum states; Variational Monte Carlo; Quantum state tomography; Machine learning; Supervised learning
Record Number:CaltechAUTHORS:20190827-093549272
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20190827-093549272
Official Citation: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, Filippo Vicentini, Alexander Wietek, NetKet: A machine learning toolkit for many-body quantum systems, SoftwareX, Volume 10, 2019, 100311, ISSN 2352-7110, https://doi.org/10.1016/j.softx.2019.100311. (http://www.sciencedirect.com/science/article/pii/S2352711019300974)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:98262
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:27 Aug 2019 17:00
Last Modified:03 Oct 2019 21:39

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