NetKet: A machine learning toolkit for many-body quantum systems
- Creators
- Carleo, Giuseppe
- Choo, Kenny
- Hofmann, Damian
- Smith, James E. T.
- Westerhout, Tom
- Alet, Fabien
- Davis, Emily J.
- Efthymiou, Stavros
- Glasser, Ivan
- Lin, Sheng-Hsuan
- Mauri, Marta
- Mazzola, Guglielmo
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Mendl, Christian B.
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van Nieuwenburg, Evert
- O'Reilly, Ossian
- Théveniaut, Hugo
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Torlai, Giacomo
- Vicentini, Filippo
- Wietek, Alexander
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.
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.Attached Files
Published - 1-s2.0-S2352711019300974-main.pdf
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Additional details
- Eprint ID
- 98262
- Resolver ID
- CaltechAUTHORS:20190827-093549272
- Flatiron Institute
- Simons Foundation
- NSF
- ACI-1547580
- Fondation CFM pour la Recherche
- European Research Council (ERC)
- 742102
- Deutsche Forschungsgemeinschaft (DFG)
- EXC-2111-390814868
- Created
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2019-08-27Created from EPrint's datestamp field
- Updated
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2023-03-16Created from EPrint's last_modified field
- Caltech groups
- Institute for Quantum Information and Matter