Machine learning algorithms for many-body quantum systems
Alternatives To Netket
Project NameStarsDownloadsRepos Using ThisPackages Using ThisMost Recent CommitTotal ReleasesLatest ReleaseOpen IssuesLicenseLanguage
Data Science Best Resources2,466
12 days ago5mit
Carefully curated resource links for data science in one place
Awesome Quantum Machine Learning2,206
3 months ago8cc0-1.0HTML
Here you can get all the Quantum Machine learning Basics, Algorithms ,Study Materials ,Projects and the descriptions of the projects around the web
Pennylane1,7851024a day ago33June 20, 2022318apache-2.0Python
PennyLane is a cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network.
Quantum1,59222 days ago8February 03, 2022125apache-2.0Python
Hybrid Quantum-Classical Machine Learning in TensorFlow
Awesome Ai Books1,086
2 months agomitJupyter Notebook
Some awesome AI related books and pdfs for learning and downloading, also apply some playground models for learning
25 days ago1December 04, 201936otherJulia
Extensible, Efficient Quantum Algorithm Design for Humans.
Strawberryfields69767a month ago25June 01, 202230apache-2.0Python
Strawberry Fields is a full-stack Python library for designing, simulating, and optimizing continuous variable (CV) quantum optical circuits.
Torchquantum67913 days ago5March 15, 202140mitPython
A PyTorch-based framework for Quantum Simulation, Quantum Machine Learning, Quantum Neural Networks, Parameterized Quantum Circuits with support for easy deployments on real quantum computers.
2 days ago138July 07, 2022138agpl-3.0Python
Pythonic tool for running machine-learning/high performance/quantum-computing workflows in heterogenous environments.
2 days ago46May 24, 2022104apache-2.0Python
Machine learning algorithms for many-body quantum systems
Alternatives To Netket
Select To Compare

Alternative Project Comparisons


Release Anaconda-Server Badge Paper (v3) codecov Slack

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. It is a Python library built on JAX.

Installation and Usage

NetKet runs on MacOS and Linux. We recommend to install NetKet using pip, but it can also be installed with conda. It is often necessary to first update pip to a recent release (>=20.3) in order for upper compatibility bounds to be considered and avoid a broken installation. For instructions on how to install the latest stable/beta release of NetKet see the Get Started page of our website or run the following command (Apple M1 users, follow that link for more instructions):

pip install --upgrade pip
pip install --upgrade netket

If you wish to install the current development version of NetKet, which is the master branch of this GitHub repository, together with the additional dependencies, you can run the following command:

pip install --upgrade pip
pip install 'git+[all]'

To speed-up NetKet-computations, even on a single machine, you can install the MPI-related dependencies by using [mpi] between square brackets.

pip install --upgrade pip
pip install --upgrade "netket[mpi]"

We recommend to install NetKet with all it's extra dependencies, which are documented below. However, if you do not have a working MPI compiler in your PATH this installation will most likely fail because it will attempt to install mpi4py, which enables MPI support in netket.

The latest release of NetKet is always available on PyPi and can be installed with pip. NetKet is also available on conda-forge, however the version available through conda install can be slightly out of date compared to PyPi. To check what is the latest version released on both distributions you can inspect the badges at the top of this readme.

Extra dependencies

When installing netket with pip, you can pass the following extra variants as square brakets. You can install several of them by separating them with a comma.

  • "[dev]": installs development-related dependencies such as black, pytest and testing dependencies
  • "[mpi]": Installs mpi4py to enable multi-process parallelism. Requires a working MPI compiler in your path
  • "[extra]": Installs tensorboardx to enable logging to tensorboard, and openfermion to convert the QubitOperators.
  • "[all]": Installs all extra dependencies

MPI Support

To enable MPI support you must install mpi4jax. Please note that we advise to install mpi4jax with the same tool (conda or pip) with which you install it's dependency mpi4py.

To check whether MPI support is enabled, check the flags

>>> import netket
>>> netket.utils.mpi.available

Installation on Windows

WARNING: Windows support is experimental, and you should expect suboptimal performance.

We suggest to use Windows Subsystem for Linux (WSL), on which you can install NetKet following the same instructions as above, and CUDA and MPI work as intended.

However, if you just want to quickly get started with NetKet, it is also possible to install it natively on Windows. First, download an unofficial build of jax from cloudhan/jax-windows-builder:

pip install --upgrade pip
pip install "jax[cpu]===0.3.25" -f --use-deprecated legacy-resolver

Alternatively, you may specify a version with CUDA support.

Then install NetKet as usual:

pip install --upgrade netket

If you want MPI support, please follow the discussion in mpi4jax.

Getting Started

To get started with NetKet, we recommend you give a look at our tutorials page, by running them on your computer or on Google Colaboratory. There are also many example scripts that you can download, run and edit that showcase some use-cases of NetKet, although they are not commented.

If you want to get in touch with us, feel free to open an issue or a discussion here on GitHub, or to join the MLQuantum slack group where several people involved with NetKet hang out. To join the slack channel just accept this invitation


Apache License 2.0

Popular Quantum Projects
Popular Machine Learning Projects
Popular Hardware Categories
Related Searches

Get A Weekly Email With Trending Projects For These Categories
No Spam. Unsubscribe easily at any time.
Machine Learning
Deep Learning
Neural Network
Machine Learning Algorithms
Physics Simulation