Solertis / netket

Machine learning algorithms for many-body quantum systems

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.

Major Features

• Graphs

  • Built-in Graphs
    • Hypercube
  • Custom Graphs
    • Any Graph With Given Adjacency Matrix [from input file]
    • Any Graph With Given Edges [from input file]
  • Symmetries
    • Automorphisms: pre-computed in built-in graphs, available through iGraph for custom graphs

• Hamiltonians

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

• Learning

  • Optimizers
    • Stochastic Gradient Descent
    • AdaMax, AdaDelta, AdaGrad, AMSGrad
    • RMSProp
    • Momentum
    • Gradient Clipping
  • 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
  • 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 wavefunction
    • Standard
    • With Permutation Symmetry Using Graph Isomorphisms
  • Custom Machines
    • Any Machine Satisfying Prototypes in AbstractMachine [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
  • Custom Sampling
    • Any k-local Stochastic Operator can be used to do Metropolis Sampling

• Statistics

  • Automatic Estimate of Correlation Times

• I/O

  • Python/JSON Interface

Installation and Usage

Please visit our homepage for further information.

License

Apache License 2.0