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

被引:81
|
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
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.
引用
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页数:8
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