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

Carleo, G., Choo, K., Hofmann, D., Smith, J. E. T., Westerhout, T., Alet, F., et al. (2019). NetKet: A machine learning toolkit for many-body quantum systems. SoftwareX, 10: 100311. doi:10.1016/j.softx.2019.100311.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0004-ADDD-1 Version Permalink: http://hdl.handle.net/21.11116/0000-0004-ADDE-0
Genre: Journal Article

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1-s2.0-S2352711019300974-main.pdf (Publisher version), 538KB
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1-s2.0-S2352711019300974-main.pdf
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This is an open access article under the CCBY license.
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Copyright Date:
2019
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© the Author(s). Published by Elsevier B.V.

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https://dx.doi.org/10.1016/j.softx.2019.100311 (Publisher version)
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 Creators:
Carleo, G.1, Author
Choo, K.2, Author
Hofmann, D.3, Author              
Smith, J. E. T.4, Author
Westerhout, T.5, Author
Alet, Fabien6, Author
Davis, E. J.7, Author
Efthymiou, S.8, Author
Glasser, I.8, Author
Lin, S.-H.9, Author
Mauri, M.1, 10, Author
Mazzola, G.11, Author
Mendl, C. B.12, Author
van Nieuwenburg, E.13, Author
O’Reilly, O.14, Author
Théveniaut, H.6, Author
Torlai, G.1, Author
Vicentini, F.15, Author
Wietek, A.1, Author
Affiliations:
1Center for Computational Quantum Physics, Flatiron Institute, ou_persistent22              
2Department of Physics, University of Zurich, ou_persistent22              
3Theoretical Description of Pump-Probe Spectroscopies in Solids, Theory Department, Max Planck Institute for the Structure and Dynamics of Matter, Max Planck Society, ou_3012828              
4Department of Chemistry, University of Colorado Boulder, ou_persistent22              
5Institute for Molecules and Materials, Radboud University, ou_persistent22              
6Laboratoire de Physique Théorique, IRSAMC, Université de Toulouse, ou_persistent22              
7Department of Physics, Stanford University, Stanford, ou_persistent22              
8Max-Planck-Institut für Quantenoptik, ou_persistent22              
9Department of Physics, T42, Technische Universität München, ou_persistent22              
10Dipartimento di Fisica, Università degli Studi di Milano, ou_persistent22              
11Theoretische Physik, ETH Zürich, ou_persistent22              
12Technische Universität Dresden, Institute of Scientific Computing, ou_persistent22              
13Institute for Quantum Information and Matter, California Institute of Technology, ou_persistent22              
14Southern California Earthquake Center, University of Southern California, ou_persistent22              
15Université de Paris, Laboratoire Matériaux et Phénomènes Quantiques, ou_persistent22              

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Free keywords: Neural-network quantum states, Variational Monte Carlo, Quantum state tomography, Machine learning, Supervised learning
 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.

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Language(s): eng - English
 Dates: 2019-08-092019-03-282019-08-122019-092019-09
 Publication Status: Published in print
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Method: Peer
 Identifiers: DOI: 10.1016/j.softx.2019.100311
 Degree: -

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Project name : 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).
Grant ID : 742102
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

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Title: SoftwareX
Source Genre: Journal
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Publ. Info: Elsevier
Pages: - Volume / Issue: 10 Sequence Number: 100311 Start / End Page: - Identifier: ISSN: 2352-7110
CoNE: https://pure.mpg.de/cone/journals/resource/2352-7110