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

• Published in: SoftwareX Vol. 10; p. 100311
• Main Authors: Carleo, Giuseppe, Choo, Kenny, Hofmann, Damian, Smith, James E.T., Westerhout, Tom, Alet, Fabien, Davis, Emily J., Efthymiou, Stavros, Glasser, Ivan, Lin, Sheng-Hsuan, Mauri, Marta, Mazzola, Guglielmo, Mendl, Christian B., van Nieuwenburg, Evert, O’Reilly, Ossian, Théveniaut, Hugo, Torlai, Giacomo, Vicentini, Filippo, Wietek, Alexander
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2019; Elsevier
• Subjects: Condensed Matter; Machine learning; Neural-network quantum states; Physics; Quantum state tomography; Supervised learning; Variational Monte Carlo; Neural-network quantum states; Supervised learning; Quantum state tomography; Variational Monte Carlo; Machine learning
• ISSN: 2352-7110; 2352-7110
• DOI: 10.1016/j.softx.2019.100311

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.