Machine learning algorithms for many-body quantum systems





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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 supports MacOS and Linux. We reccomend to install NetKet using pip For instructions on how to install the latest stable/beta release of NetKet see the Getting Started section of our website.

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 'git+[all]'

You can also install the MPI-related dependencies by using [dev,mpi] between the square brackets. 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 not currently available on conda-forge. However, you can still install NetKet with pip inside conda environments.

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
  • '[tensorboard]': Installs tensorboardx to enable logging to tensorboard.
  • '[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 whever MPI support is enabled, check the flags

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

Major Features

  • Graphs

    • Built-in Graphs
      • Hypercube
      • General Lattice with arbitrary number of atoms per unit cell
    • Custom Graphs
      • Any Graph With Given Adjacency Matrix
      • Any Graph With Given Edges
    • Symmetries
      • Automorphisms: pre-computed in built-in graphs, available through iGraph for custom graphs
  • Quantum Operators

    • Built-in Hamiltonians
      • Transverse-field Ising
      • Heisenberg
      • Bose-Hubbard
    • Custom Operators
      • Any k-local Hamiltonian
      • General k-local Operator defined on Graphs
  • Variational Monte Carlo

    • Stochastic Learning Methods for Ground-State Problems
      • Gradient Descent
      • Stochastic Reconfiguration Method
        • Direct Solver
        • Iterative Solver for Large Number of Parameters
  • Exact Diagonalization

    • Full Solver
    • Lanczos Solver
    • Imaginary-Time Dynamics
  • Supervised Learning

    • Supervised overlap optimization from given data
  • Neural-Network Quantum State Tomography

    • Using arbitrary k-local measurement basis
  • Optimizers

    • Stochastic Gradient Descent
    • AdaMax, AdaDelta, AdaGrad, AMSGrad
    • RMSProp
    • Momentum
  • Models

    • Restricted Boltzmann Machines
      • Standard
      • For Custom Local Hilbert Spaces
      • With Permutation Symmetry Using Graph Isomorphisms
    • Feed-Forward Networks
      • For Custom Local Hilbert Spaces
    • Jastrow States
      • Standard
      • With Permutation Symmetry Using Graph Isomorphisms
    • Matrix Product States
      • MPS
      • Periodic MPS
    • Custom Models
  • Observables

    • Custom Observables
      • Any k-local Operator
  • Sampling

    • Local Metropolis Moves
      • Local Hilbert Space Sampling
    • Hamiltonian Moves
      • Automatic Moves with Hamiltonian Symmetry
    • Custom Sampling
      • Any k-local Stochastic Operator can be used to do Metropolis Sampling
    • Exact Sampler for small systems
  • Statistics

    • Automatic Estimate of Correlation Times
  • Interface

    • Python module
    • JSON output


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