Source code for netket.operator._kinetic

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# Licensed under the Apache License, Version 2.0 (the "License");
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from typing import Optional, Callable, Union, List
from functools import partial

import numpy as np

import jax
import jax.numpy as jnp

from netket.utils.types import DType, PyTree, Array
from netket.hilbert import AbstractHilbert
from netket.operator import ContinuousOperator


class KineticEnergy(ContinuousOperator):
    r"""This is the kinetic energy operator (hbar = 1). The local value is given by:
    :math:`E_{kin} = -1/2 ( \sum_i \frac{1}{m_i} (\log(\psi))'^2 + (\log(\psi))'' )`
    """

[docs] def __init__( self, hilbert: AbstractHilbert, mass: Union[float, List[float]], dtype: Optional[DType] = None, ): r"""Args: hilbert: The underlying Hilbert space on which the operator is defined mass: float if all masses are the same, list indicating the mass of each particle otherwise dtype: Data type of the matrix elements. Defaults to `np.float64` """ self._mass = jnp.asarray(mass, dtype=dtype) self._is_hermitian = np.allclose(self._mass.imag, 0.0) super().__init__(hilbert, self._mass.dtype)
@property def mass(self): return self._mass @property def is_hermitian(self): return self._is_hermitian def _expect_kernel( self, logpsi: Callable, params: PyTree, x: Array, mass: Optional[PyTree] ): def logpsi_x(x): return logpsi(params, x) dlogpsi_x = jax.grad(logpsi_x) basis = jnp.eye(x.shape[0]) y, f_jvp = jax.linearize(dlogpsi_x, x) dp_dx2 = jnp.diag(jax.vmap(f_jvp)(basis)) dp_dx = dlogpsi_x(x) ** 2 return -0.5 * jnp.sum(mass * (dp_dx2 + dp_dx), axis=-1) @partial(jax.vmap, in_axes=(None, None, None, 0, None)) def _expect_kernel_batched( self, logpsi: Callable, params: PyTree, x: Array, coefficient: Optional[PyTree] ): return self._expect_kernel(logpsi, params, x, coefficient) def _pack_arguments(self) -> PyTree: return 1.0 / self._mass def __repr__(self): return f"KineticEnergy(m={self._mass})"