netket.models#
This sub-module contains several pre-built models to be used as neural quantum states.
_Exact_ ansatz storing the logarithm of the full, exponentially large wavefunction coefficients. |
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A restricted boltzman Machine, equivalent to a 2-layer FFNN with a nonlinear activation function in between. |
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A fully connected Restricted Boltzmann Machine (RBM) with real-valued parameters. |
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A fully connected Restricted Boltzmann Machine (see |
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A symmetrized RBM using the netket.nn.DenseSymm layer internally. |
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Jastrow wave function \(\Psi(s) = \exp(\sum_{ij} s_i W_{ij} s_j)\). |
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A periodic Matrix Product State (MPS) for a quantum state of discrete degrees of freedom, wrapped as Jax machine. |
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Encodes a Positive-Definite Neural Density Matrix using the ansatz from Torlai and Melko, PRL 120, 240503 (2018). |
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Implements a Group Convolutional Neural Network (G-CNN) that outputs a wavefunction that is invariant over a specified symmetry group. |
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Base class for autoregressive neural networks. |
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Autoregressive neural network with dense layers. |
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Autoregressive neural network with 1D convolution layers. |
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Autoregressive neural network with 2D convolution layers. |
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Fast autoregressive neural network with 1D convolution layers. |
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Fast autoregressive neural network with 2D convolution layers. |
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Implements the DeepSets architecture, which is permutation invariant. |
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A Multi-Layer Perceptron with hidden layers. |
The following models are particularly suited for systems with continuous degrees of freedom (:class:nk.hilbert.Particle
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Multivariate Gaussian function with mean 0 and parametrised covariance matrix \(\Sigma_{ij}\). |
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Implements an equivariant version of the DeepSets architecture given by (https://arxiv.org/abs/1703.06114) |