CaltechAUTHORS
Published July 2019 | Version Published
Journal Article Open

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

Creators

  • 1. ROR icon University of Zurich
  • 2. ROR icon Max Planck Institute for the Structure and Dynamics of Matter
  • 3. ROR icon University of Colorado Boulder
  • 4. ROR icon Radboud University Nijmegen
  • 5. ROR icon Laboratoire de Physique Théorique
  • 6. ROR icon Stanford University
  • 7. ROR icon Max Planck Institute of Quantum Optics
  • 8. ROR icon Technical University of Munich
  • 9. ROR icon University of Milan
  • 10. ROR icon ETH Zurich
  • 11. ROR icon TU Dresden
  • 12. ROR icon California Institute of Technology
  • 13. ROR icon University of Southern California
  • 14. ROR icon Laboratory Materials and Quantum Phenomena

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

Files

1-s2.0-S2352711019300974-main.pdf

Files (550.8 kB)

Name Size
md5:158a96eb888ba4650bf437de8aeb97e6
550.8 kB Preview Download

Additional details

Identifiers

Eprint ID
98262
Resolver ID
CaltechAUTHORS:20190827-093549272

Funding

Flatiron Institute
Simons Foundation
NSF
ACI-1547580
Fondation CFM pour la Recherche
European Research Council (ERC)
742102
Deutsche Forschungsgemeinschaft (DFG)
EXC-2111-390814868

Dates

Created
2019-08-27
Created from EPrint's datestamp field
Updated
2023-03-16
Created from EPrint's last_modified field

Caltech Custom Metadata

Caltech groups
Institute for Quantum Information and Matter