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

被引：90

作者：

Carleo, Giuseppe ^{[1
]}

Choo, Kenny ^{[2
]}

Hofmann, Damian ^{[3
]}

Smith, James E. T. ^{[4
]}

Westerhout, Tom ^{[5
]}

Alet, Fabien ^{[6
]}

Davis, Emily J. ^{[7
]}

Efthymiou, Stavros ^{[8
]}

Glasser, Ivan ^{[8
]}

Lin, Sheng-Hsuan ^{[9
]}

Mauri, Marta ^{[1
,10
]}

Mazzola, Guglielmo ^{[11
]}

Mendl, Christian B. ^{[12
]}

van Nieuwenburg, Evert ^{[13
]}

O'Reilly, Ossian ^{[14
]}

Theveniaut, Hugo ^{[6
]}

Torlai, Giacomo ^{[1
]}

Vicentini, Filippo ^{[15
]}

Wietek, Alexander ^{[1
]}

机构：

[1] Flatiron Inst, Ctr Computat Quantum Phys, 162 5th Ave, New York, NY 10010 USA

[2] Univ Zurich, Dept Phys, Winterthurerstr 190, CH-8057 Zurich, Switzerland

[3] Max Planck Inst Struct & Dynam Matter, Luruper Chaussee 149, D-22761 Hamburg, Germany

[4] Univ Colorado, Dept Chem, Boulder, CO 80302 USA

[5] Radboud Univ Nijmegen, Inst Mol & Mat, NL-6525 AJ Nijmegen, Netherlands

[6] Univ Toulouse, Lab Phys Theor, IRSAMC, CNRS,UPS, F-31062 Toulouse, France

[7] Stanford Univ, Dept Phys, Stanford, CA 94305 USA

[8] Max Planck Inst Quantum Opt, Hans Kopfermann Str 1, D-85748 Garching, Germany

[9] Tech Univ Munich, Dept Phys, T42,James Franck Str 1, D-85748 Garching, Germany

[10] Univ Milan, Dipartimento Fis, Via Celoria 16, I-20133 Milan, Italy

[11] Swiss Fed Inst Technol, Theoret Phys, CH-8093 Zurich, Switzerland

[12] Tech Univ Dresden, Inst Sci Comp, Zellescher Weg 12-14, D-01069 Dresden, Germany

[13] CALTECH, Inst Quantum Informat & Matter, Pasadena, CA 91125 USA

[14] Univ Southern Calif, Southern Calif Earthquake Ctr, 3651 Trousdale Pkwy, Los Angeles, CA 90089 USA

[15] Univ Paris, Lab Mat & Phenomenes Quant, CNRS, F-75013 Paris, France

来源：

SOFTWAREX | 2019年 / 10卷

关键词：

Neural-network quantum states; Variational Monte Carlo; Quantum state tomography; Machine learning; Supervised learning; WAVE-FUNCTIONS; MONTE-CARLO;

D O I：

10.1016/j.softx.2019.100311

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

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. (C) 2019 The Authors. Published by Elsevier B.V.

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