  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.
