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.

• Graphs

  • Built-in Graphs
    • Hypercube
  • Custom Graphs
    • Any Graph With Given Adjacency Matrix [from input file]
    • Any Graph With Given Edges [from input file]

• Hamiltonians

  • Built-in Hamiltonians
    • Transverse-field Ising
    • Heisenberg
    • Bose-Hubbard
  • Custom Hamiltonians
    • Any k-local Hamiltonian [from input file]

• Learning

  • Steppers
    • Stochastic Gradient Descent
    • AdaMax
  • Ground-state Learning
    • Gradient Descent
    • Stochastic Reconfiguration Method
      • Direct Solver
      • Iterative Solver for Large Number of Parameters

• Machines

  • Restricted Boltzmann Machines
    • Standard
    • For Custom Local Hilbert Spaces
    • With Permutation Symmetry Using Graph Isomorphisms
  • Custom Machines
    • Any Machine Satisfying Prototype of Abstract Machine [extending C++ code]

• Observables

  • Custom Observables
    • Any k-local Operator [from input file]

• Sampling

  • Local Metropolis Moves
    • Local Hilbert Space Sampling
    • Parallel Tempering Versions
  • Hamiltonian Moves
    • Automatic Moves with Hamiltonian Symmetry
    • Parallel Tempering Versions

• Statistics

  • Automatic Estimate of Correlation Times

• I/O

  • Python/JSON Interface

Please visit our homepage for further information.

Apache License 2.0