[netket - the machine learning toolbox for quantum physics NetKet is an open-source project delivering cutting-edge methods for the study of many-body quantum systems with artificial neural networks and machine learning techniques. NetKet provides state-of-the-art Neural-Network Quantum states, and advanced learning algorithms to find the ground-state of many-body Hamiltonians. NetKet provides a modular infrastructure for the development and application of machine-learning techniques to many-body quantum systems. You can set up your custom many-body Hamiltonian, observables, lattices, and machines in minutes. The learning algorithms used in NetKet are intrinsically amenable to massive parallelism. NetKet is built using MPI primitives, and can scale up to thousands of CPU cores. Find out our challenges and get involved in the NetKet Project now. Contributing developers will author a paper describing the NetKet library. NetKet is supported by the Simons Foundation and the Flatiron Institute. NetKet’s developer lead and founder is Giuseppe Carleo.]