Source code for pennylane.optimize.nesterov_momentum

# Copyright 2018-2021 Xanadu Quantum Technologies Inc.

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"""Nesterov momentum optimizer"""
from pennylane._grad import grad as get_gradient
from .momentum import MomentumOptimizer

[docs]class NesterovMomentumOptimizer(MomentumOptimizer): r"""Gradient-descent optimizer with Nesterov momentum. Nesterov Momentum works like the :class:`Momentum optimizer <.pennylane.optimize.MomentumOptimizer>`, but shifts the current input by the momentum term when computing the gradient of the objective function: .. math:: a^{(t+1)} = m a^{(t)} + \eta \nabla f(x^{(t)} - m a^{(t)}). The user defined parameters are: * :math:`\eta`: the step size * :math:`m`: the momentum Args: stepsize (float): user-defined hyperparameter :math:`\eta` momentum (float): user-defined hyperparameter :math:`m` """
[docs] def compute_grad( self, objective_fn, args, kwargs, grad_fn=None ): # pylint: disable=arguments-renamed r"""Compute gradient of the objective function at at the shifted point :math:`(x - m\times\text{accumulation})` and return it along with the objective function forward pass (if available). Args: objective_fn (function): the objective function for optimization. args (tuple): tuple of NumPy arrays containing the current values for the objection function. kwargs (dict): keyword arguments for the objective function. grad_fn (function): optional gradient function of the objective function with respect to the variables ``x``. If ``None``, the gradient function is computed automatically. Must return the same shape of tuple [array] as the autograd derivative. Returns: tuple [array]: the NumPy array containing the gradient :math:`\nabla f(x^{(t)})` and the objective function output. If ``grad_fn`` is provided, the objective function will not be evaluted and instead ``None`` will be returned. """ shifted_args = list(args) trainable_indices = [ i for i, arg in enumerate(args) if getattr(arg, "requires_grad", False) ] if self.accumulation: for index in trainable_indices: shifted_args[index] = args[index] - self.momentum * self.accumulation[index] g = get_gradient(objective_fn) if grad_fn is None else grad_fn grad = g(*shifted_args, **kwargs) forward = getattr(g, "forward", None) grad = (grad,) if len(trainable_indices) == 1 else grad return grad, forward