Source code for netket.vqs.mc.common

# Copyright 2021 The NetKet Authors - All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#    http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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from typing import Any

import jax
import jax.numpy as jnp

from netket.hilbert import AbstractHilbert
from netket.utils.dispatch import dispatch


def check_hilbert(A: AbstractHilbert, B: AbstractHilbert):
    if not A == B:
        raise NotImplementedError(  # pragma: no cover
            f"Non matching hilbert spaces {A} and {B}"
        )


[docs] @dispatch.abstract def get_local_kernel_arguments(vstate: Any, : Any): """ Returns the samples of vstate used to compute the expectation value of the operator O, and the connected elements and matrix elements. Args: vstate: the variational state Ô: the operator Returns: A Tuple with 2 elements (sigma, args), where the first elements should be the samples over which the classical expectation value should be computed, while the latter is anything that can be fed as input to the local_kernel. """
[docs] @dispatch.abstract def get_local_kernel(vstate: Any, : Any): """ Returns the function computing the local estimator for the given variational state and operator. Args: vstate: the variational state Ô: the operator Returns: A callable accepting the output of `get_configs(vstate, O)`. """
@jax.jit def force_to_grad(Ō_grad, parameters): """ Converts the forces vector F_k = cov(O_k, E_loc) to the observable gradient. In case of a complex target (which we assume to correspond to a holomorphic parametrization), this is the identity. For real-valued parameters, the gradient is 2 Re[F]. """ Ō_grad = jax.tree_util.tree_map( lambda x, target: (x if jnp.iscomplexobj(target) else 2 * x.real).astype( target.dtype ), Ō_grad, parameters, ) return Ō_grad