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arXiv · 1904.00031

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·Alexander Wietek
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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 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.

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BibTeXRIS

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, Alexander Wietek. 2019-03-29. NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems. https://doi.org/10.1016/j.softx.2019.100311

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