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  NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems

Vicentini, F., Hofmann, D., Szabó, A., Wu, D., Roth, C., Giuliani, C., et al. (2022). NetKet 3: Machine Learning Toolbox for Many-Body Quantum Systems. SciPost Physics Codebases, 7. doi:10.21468/SciPostPhysCodeb.7.

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Item Permalink: https://hdl.handle.net/21.11116/0000-000A-94B8-F Version Permalink: https://hdl.handle.net/21.11116/0000-000E-6773-D
Genre: Journal Article

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SciPostPhysCodeb_7.pdf (Publisher version), 843KB
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https://arxiv.org/abs/2112.10526 (Preprint)
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https://doi.org/10.21468/SciPostPhysCodeb.7 (Publisher version)
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https://doi.org/10.21468/SciPostPhysCodeb.7-r3.4 (Code)
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 Creators:
Vicentini, F.1, 2, Author
Hofmann, D.3, 4, Author           
Szabó, A.5, 6, Author
Wu, D.1, 2, Author
Roth, C.7, Author
Giuliani, C.1, 2, Author
Pescia, G.1, 2, Author
Nys, J.1, 2, Author
Vargas-Calderón, V.8, Author
Astrakhantsev, N.9, Author
Carleo, G.1, 2, Author
Affiliations:
1École Polytechnique Fédérale de Lausanne (EPFL), Institute of Physics, ou_persistent22              
2Center for Quantum Science and Engineering, École Polytechnique Fédérale de Lausanne (EPFL), 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              
4Center for Free-Electron Laser Science (CFEL), ou_persistent22              
5Rudolf Peierls Centre for Theoretical Physics, University of Oxford, ou_persistent22              
6ISIS Facility, Rutherford Appleton Laboratory, Harwell Campus, ou_persistent22              
7Physics Department, University of Texas at Austin, ou_persistent22              
8Grupo de Superconductividad y Nanotecnología, Departamento de Física, Universidad Nacional de Colombia, ou_persistent22              
9Department of Physics, University of Zurich, ou_persistent22              

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 Abstract: We introduce version 3 of NetKet, the machine learning toolbox for many-body quantum physics. NetKet is built around neural-network quantum states and provides efficient algorithms for their evaluation and optimization. This new version is built on top of JAX, a differentiable programming and accelerated linear algebra framework for the Python programming language. The most significant new feature is the possibility to define arbitrary neural network ansätze in pure Python code using the concise notation of machine-learning frameworks, which allows for just-in-time compilation as well as the implicit generation of gradients thanks to automatic differentiation. NetKet 3 also comes with support for GPU and TPU accelerators, advanced support for discrete symmetry groups, chunking to scale up to thousands of degrees of freedom, drivers for quantum dynamics applications, and improved modularity, allowing users to use only parts of the toolbox as a foundation for their own code.

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Language(s): eng - English
 Dates: 2021-12-202022-06-132022-08-24
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: arXiv: 2112.10526
DOI: 10.21468/SciPostPhysCodeb.7
 Degree: -

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Project name : This work is supported by the Swiss National Science Foundation un- der Grant No. 200021_200336. D. H. acknowledges support by the Max Planck-New York City Center for Nonequilibrium Quantum Phenomena. A. Sz. gratefully acknowledges the ISIS Neu- tron and Muon Source and the Oxford–ShanghaiTech collaboration for support of the Keeley– Rutherford fellowship at Wadham College, Oxford. J. N. was supported by Microsoft Research.
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Source 1

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Title: SciPost Physics Codebases
  Abbreviation : SciPost Phys. Codebases
Source Genre: Journal
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Publ. Info: Amsterdam : SciPost Foundation
Pages: - Volume / Issue: 7 Sequence Number: - Start / End Page: - Identifier: CoNE: https://pure.mpg.de/cone/journals/resource/SciPostPhysCodeb