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Machine learning algorithms for many-body quantum systems
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README.md

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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.

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

Installation and Usage

Please visit our homepage for further information.

License

Apache License 2.0

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