NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems
Quantum Physics2019-09-06v1Disordered Systems and Neural NetworksStrongly Correlated ElectronsComputational PhysicsData Analysis, Statistics and Probability
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.
@article{arxiv.1904.00031,
title = {NetKet: A Machine Learning Toolkit for Many-Body Quantum Systems},
author = {Giuseppe Carleo and Kenny Choo and Damian Hofmann and James E. T. Smith and Tom Westerhout and Fabien Alet and Emily J. Davis and Stavros Efthymiou and Ivan Glasser and Sheng-Hsuan Lin and Marta Mauri and Guglielmo Mazzola and Christian B. Mendl and Evert van Nieuwenburg and Ossian O'Reilly and Hugo Théveniaut and Giacomo Torlai and Alexander Wietek},
journal= {arXiv preprint arXiv:1904.00031},
year = {2019}
}