NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems

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 wave funct...

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Published inarXiv.org
Main Authors Carleo, Giuseppe, Choo, Kenny, Hofmann, Damian, Smith, James E T, Westerhout, Tom, Alet, Fabien, Davis, Emily J, Efthymiou, Stavros, Glasser, Ivan, Sheng-Hsuan Lin, Mauri, Marta, Mazzola, Guglielmo, Mendl, Christian B, Evert van Nieuwenburg, O'Reilly, Ossian, Théveniaut, Hugo, Torlai, Giacomo, Wietek, Alexander
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 29.03.2019
Subjects
Algorithms
Artificial intelligence
Machine learning
Neural networks
Physics - Computational Physics
Physics - Data Analysis, Statistics and Probability
Physics - Disordered Systems and Neural Networks
Physics - Quantum Physics
Physics - Strongly Correlated Electrons
Quantum theory
Wave functions
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Summary: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 wave functions. NetKet provides algorithms for several key tasks in quantum many-body physics and quantum technology, namely quantum state tomography, supervised learning from wave-function 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.
ISSN:2331-8422
DOI:10.48550/arxiv.1904.00031