NetKet - The Machine Learning Toolbox for Quantum Physics

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.
Neural Quantum States NetKet provides state-of-the-art Neural-Network Quantum states, and advanced learning algorithms to study many-body quantum systems. Jax-powered Netket is based on Jax, therefore you can run on CPUs, GPUs and TPUs, using Sharding or MPI to distribute among multiple nodes. Interoperable NetKet can easily interoperate with other tools such as OpenFermion or QuTiP. Multi-framework NetKet’s built in models are implemented using Flax, but it is also compatible with Equinox, Haiku and can be made compatible with future frameworks. Easy to use NetKet has an high-level syntax that is easy to use, while exposing low level objects that can be combined together to develop novel algorithms. Open source NetKet is a collaborative effort by a group of researchers. We are constantly looking for new contributors. Do get in touch with us through the Github Discussions or through our Slack Channel
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