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

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

doi:10.1016/j.softx.2019.100311

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

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

Publikation: Beitrag in Fachzeitschrift › Artikel › Begutachtung

71 Zitate (Scopus)

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.

Originalsprache	Englisch
Aufsatznummer	100311
Fachzeitschrift	SoftwareX
Jahrgang	10
DOIs	https://doi.org/10.1016/j.softx.2019.100311
Publikationsstatus	Veröffentlicht - 1 Juli 2019
Extern publiziert	Ja

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10.1016/j.softx.2019.100311

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Carleo, G., Choo, K., Hofmann, D., Smith, J. E. T., Westerhout, T., Alet, F., Davis, E. J., Efthymiou, S., Glasser, I., Lin, S. H., Mauri, M., Mazzola, G., Mendl, C. B., van Nieuwenburg, E., O'Reilly, O., Théveniaut, H., Torlai, G., Vicentini, F., & Wietek, A. (2019). NetKet: A machine learning toolkit for many-body quantum systems. SoftwareX, 10, Artikel 100311. https://doi.org/10.1016/j.softx.2019.100311

@article{b1eb054337834051a30d867dafd456d9,

title = "NetKet: A machine learning toolkit for many-body quantum systems",

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.",

keywords = "Machine learning, Neural-network quantum states, Quantum state tomography, Supervised learning, Variational Monte Carlo",

author = "Giuseppe Carleo and Kenny Choo and Damian Hofmann and Smith, {James E.T.} and Tom Westerhout and Fabien Alet and Davis, {Emily J.} and Stavros Efthymiou and Ivan Glasser and Lin, {Sheng Hsuan} and Marta Mauri and Guglielmo Mazzola and Mendl, {Christian B.} and {van Nieuwenburg}, Evert and Ossian O'Reilly and Hugo Th{\'e}veniaut and Giacomo Torlai and Filippo Vicentini and Alexander Wietek",

note = "Publisher Copyright: {\textcopyright} 2019 The Authors",

year = "2019",

month = jul,

day = "1",

doi = "10.1016/j.softx.2019.100311",

language = "English",

volume = "10",

journal = "SoftwareX",

issn = "2352-7110",

publisher = "Elsevier BV",

}

Carleo, G, Choo, K, Hofmann, D, Smith, JET, Westerhout, T, Alet, F, Davis, EJ, Efthymiou, S, Glasser, I, Lin, SH, Mauri, M, Mazzola, G, Mendl, CB, van Nieuwenburg, E, O'Reilly, O, Théveniaut, H, Torlai, G, Vicentini, F & Wietek, A 2019, 'NetKet: A machine learning toolkit for many-body quantum systems', SoftwareX, Jg. 10, 100311. https://doi.org/10.1016/j.softx.2019.100311

TY - JOUR

T1 - NetKet

T2 - A machine learning toolkit for many-body quantum systems

AU - Carleo, Giuseppe

AU - Choo, Kenny

AU - Hofmann, Damian

AU - Smith, James E.T.

AU - Westerhout, Tom

AU - Alet, Fabien

AU - Davis, Emily J.

AU - Efthymiou, Stavros

AU - Glasser, Ivan

AU - Lin, Sheng Hsuan

AU - Mauri, Marta

AU - Mazzola, Guglielmo

AU - Mendl, Christian B.

AU - van Nieuwenburg, Evert

AU - O'Reilly, Ossian

AU - Théveniaut, Hugo

AU - Torlai, Giacomo

AU - Vicentini, Filippo

AU - Wietek, Alexander

PY - 2019/7/1

Y1 - 2019/7/1

N2 - 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.

AB - 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.

KW - Machine learning

KW - Neural-network quantum states

KW - Quantum state tomography

KW - Supervised learning

KW - Variational Monte Carlo

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U2 - 10.1016/j.softx.2019.100311

DO - 10.1016/j.softx.2019.100311

M3 - Article

AN - SCOPUS:85071244765

SN - 2352-7110

VL - 10

JO - SoftwareX

JF - SoftwareX

M1 - 100311

ER -

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

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