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

• Main Authors: 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, Filippo Vicentini, Alexander Wietek
• Format: Article
• Language: English
• Published: Elsevier 2019-07-01
• Series: SoftwareX
• Online Access: http://www.sciencedirect.com/science/article/pii/S2352711019300974

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. Keywords: Neural-network quantum states, Variational Monte Carlo, Quantum state tomography, Machine learning, Supervised learning