qml.MomentumOptimizer¶

class MomentumOptimizer(stepsize=0.01, momentum=0.9)[source]¶

Bases: GradientDescentOptimizer

Gradient-descent optimizer with momentum.

The momentum optimizer adds a “momentum” term to gradient descent which considers the past gradients:

$x^{(t+1)} = x^{(t)} - a^{(t+1)}.$

The accumulator term $a$ is updated as follows:

$a^{(t+1)} = m a^{(t)} + \eta \nabla f(x^{(t)}),$

with user defined parameters:

$\eta$ : the step size
$m$ : the momentum

Parameters:

stepsize (float) – user-defined hyperparameter $\eta$
momentum (float) – user-defined hyperparameter $m$

Note

When using torch, tensorflow or jax interfaces, refer to Gradients and training for suitable optimizers.

Methods

`apply_grad`(grad, args)	Update the trainable args to take a single optimization step.
`compute_grad`(objective_fn, args, kwargs[, ...])	Compute the gradient of the objective function at the given point and return it along with the objective function forward pass (if available).
`reset`()	Reset optimizer by erasing memory of past steps.
`step`(objective_fn, *args[, grad_fn])	Update trainable arguments with one step of the optimizer.
`step_and_cost`(objective_fn, *args[, grad_fn])	Update trainable arguments with one step of the optimizer and return the corresponding objective function value prior to the step.

apply_grad(grad, args)[source]¶

Update the trainable args to take a single optimization step. Flattens and unflattens the inputs to maintain nested iterables as the parameters of the optimization.

Parameters:

grad (tuple [array]) – the gradient of the objective function at point $x^{(t)}$ : $\nabla f(x^{(t)})$ .
args (tuple) – the current value of the variables $x^{(t)}$ .

Returns:

the new values $x^{(t+1)}$ .

Return type:

list [array]

static compute_grad(objective_fn, args, kwargs, grad_fn=None)¶

Compute the gradient of the objective function at the given point and return it along with the objective function forward pass (if available).

Parameters:

objective_fn (function) – the objective function for optimization
args (tuple) – tuple of NumPy arrays containing the current parameters 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 args. If None, the gradient function is computed automatically. Must return the same shape of tuple [array] as the autograd derivative.

Returns:

NumPy array containing the gradient $\nabla f(x^{(t)})$ and the objective function output. If grad_fn is provided, the objective function will not be evaluated and instead None will be returned.

Return type:

tuple (array)

reset()[source]¶: Reset optimizer by erasing memory of past steps.

step(objective_fn, *args, grad_fn=None, **kwargs)¶

Update trainable arguments with one step of the optimizer.

Parameters:

objective_fn (function) – the objective function for optimization
*args – Variable length argument list for 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 a tuple[array] with the same number of elements as *args. Each array of the tuple should have the same shape as the corresponding argument.
**kwargs – variable length of keyword arguments for the objective function

Returns:

the new variable values $x^{(t+1)}$ . If single arg is provided, list [array] is replaced by array.

Return type:

list [array]

step_and_cost(objective_fn, *args, grad_fn=None, **kwargs)¶

Update trainable arguments with one step of the optimizer and return the corresponding objective function value prior to the step.

Parameters:

objective_fn (function) – the objective function for optimization
*args – variable length argument list for objective function
grad_fn (function) – optional gradient function of the objective function with respect to the variables *args. If None, the gradient function is computed automatically. Must return a tuple[array] with the same number of elements as *args. Each array of the tuple should have the same shape as the corresponding argument.
**kwargs – variable length of keyword arguments for the objective function

Returns:

the new variable values $x^{(t+1)}$ and the objective function output prior to the step. If single arg is provided, list [array] is replaced by array.

Return type:

tuple[list [array], float]

qml.MomentumOptimizer¶

Methods

Contents