netket.models

netket.models#

This sub-module contains several pre-built models to be used as neural quantum states.

Generic models#

This section lists some simple variational architectures.

`LogStateVector`	_Exact_ ansatz storing the logarithm of the full, exponentially large wavefunction coefficients.
`RBM`	A restricted boltzman Machine, equivalent to a 2-layer FFNN with a nonlinear activation function in between.
`RBMModPhase`	A fully connected Restricted Boltzmann Machine (RBM) with real-valued parameters.
`RBMMultiVal`	A fully connected Restricted Boltzmann Machine (see `netket.models.RBM`) suitable for large local hilbert spaces.
`RBMSymm`	A symmetrized RBM using the `netket.nn.DenseSymm()` layer internally.
`Jastrow`	Jastrow wave function \(\Psi(s) = \exp(\sum_{i \neq j} s_i W_{ij} s_j)\), where W is a symmetric matrix.
`NDM`	Encodes a Positive-Definite Neural Density Matrix using the ansatz from Torlai and Melko, PRL 120, 240503 (2018).
`GCNN`	Implements a Group Convolutional Neural Network (G-CNN) that outputs a wavefunction that is invariant over a specified symmetry group.
`DeepSetMLP`	Implements the DeepSets architecture, which is permutation invariant.
`MLP`	A Multi-Layer Perceptron with hidden layers.

Autoregressive models#

The following autoregressive models can be directly sampled using ARDirectSampler.

Those that follow are the abstract classes that can be inherited from in order to build an autoregressive model.

`AbstractARNN`	Base class for autoregressive neural networks.
`ARNNSequential`	Implementation of an ARNN that sequentially calls its layers, and optionally an activation function.
`FastARNNSequential`	Implementation of a fast ARNN that sequentially calls its layers and activation function.

And those are some default implementation of Dense and Convolutional-autoregressive Neural networks. Those are built by using masked dense and masked convolutional layers.

`ARNNDense`	Autoregressive neural network with dense layers.
`ARNNConv1D`	Autoregressive neural network with 1D convolution layers.
`ARNNConv2D`	Autoregressive neural network with 2D convolution layers.
`FastARNNConv1D`	Fast autoregressive neural network with 1D convolution layers.
`FastARNNConv2D`	Fast autoregressive neural network with 2D convolution layers.

Continuous degrees of freedom#

The following models are particularly suited for systems with continuous degrees of freedom (Particle)

`Gaussian`	Multivariate Gaussian function with mean 0 and parametrised covariance matrix \(\Sigma_{ij}\).
`DeepSetRelDistance`	Implements an equivariant version of the DeepSets architecture given by (https://arxiv.org/abs/1703.06114)

Tensor Networks#

The following models are tensor networks, that can be used as variational ansatzes:

`tensor_networks.MPSOpen`	An open Matrix Product State (MPS) for a quantum state of discrete degrees of freedom.
`tensor_networks.MPSPeriodic`	A periodic Matrix Product State (MPS) for a quantum state of discrete degrees of freedom.
`tensor_networks.MPDOOpen`	A Matrix Product Density Operator (MPDO) with open boundary conditions for a quantum mixed state of discrete degrees of freedom.
`tensor_networks.MPDOPeriodic`	A Matrix Product Density Operator (MPDO) with periodic boundary conditions for a quantum mixed state of discrete degrees of freedom.

Experimental models#

The following models are experimental, meaning that we could change them at some point, and we actively seeking for feedback and opinions on their usage and APIs.

Fermionic models#

The following models are for 2nd-quantisation fermionic hilbert spaces (SpinOrbitalFermions).

`Slater2nd`	A slater determinant ansatz for second-quantised spinless or spin-full fermions.
`MultiSlater2nd`	A slater determinant ansatz for second-quantised spinless or spin-full fermions with a sum of determinants.

Recurrent Neural Networks (RNN)#

The following are abstract models for Recurrent neural networks (and their fast versions).

`RNN`	Base class for recurrent neural networks.
`FastRNN`	Base class for recurrent neural networks with fast sampling.

The following are concrete, ready to use versions of Recurrent Neural Networks

`LSTMNet`	Long short-term memory network.
`FastLSTMNet`	Long short-term memory network with fast sampling.
`GRUNet1D`	Gated recurrent unit network.
`FastGRUNet1D`	Gated recurrent unit network with fast sampling.