Machine Learning for Many-Body Quantum Systems
What is NetKet?
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.
Learn Quantum States
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.
Find out our challenges and get involved in the NetKet Project now. Contributing developers will author a paper describing the NetKet library.
Run in Parallel
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.
C++ and Python
NetKet is built upon a fast C++ core, but also provides a Python interface. The interface is meant to simplify day-to-day operations, and allows to provide custom Hamiltonians, Observables, etc without ever touching the C++ code.
NetKet wants to be a common platform for the development of new algorithms to study the most challenging open problems in many-body quantum physics. Building upon a set of well-tested primitives and on a solid infrastructure, researchers can get publication-grade results in less time.