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. It is a Python library built on C++ primitives.

• Homepage: https://netket.org
• Citing: https://www.netket.org/citing
• Documentation: https://netket.org/documentation
• Tutorials: https://www.netket.org/tutorials
• Examples: https://github.com/netket/netket/tree/master/Examples
• Source code: https://github.com/netket/netket

Installation and Usage

You can install on osx or linux with either

• pip : pip install netket
• conda : conda install conda-forge::netket

Conda by default ships pre-built binaries for recent versions of python. The default blas library is openblas, but mkl can be enforced.

To learn more, check out the website or the examples.

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

• Machines

  • Restricted Boltzmann Machines
    • Standard
    • For Custom Local Hilbert Spaces
    • With Permutation Symmetry Using Graph Isomorphisms
  • Feed-Forward Networks
    • For Custom Local Hilbert Spaces
    • Fully connected layer
    • Convnet layer for arbitrary underlying graph
    • Any Layer Satisfying Prototypes in AbstractLayer [extending C++ code]
  • Jastrow States
    • Standard
    • With Permutation Symmetry Using Graph Isomorphisms
  • Matrix Product States
    • MPS
    • Periodic MPS
  • Custom Machines
    • Any Machine Satisfying Prototypes in AbstractMachine [extending C++ code]

• 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 Library
  • JSON output

License

Apache License 2.0