qiskit.aqua.components.optimizers.AQGD

class AQGD(maxiter=1000, eta=1.0, tol=1e-06, disp=False, momentum=0.25, param_tol=1e-06, averaging=10)

Analytic Quantum Gradient Descent (AQGD) with Epochs optimizer. Performs gradient descent optimization with a momentum term, analytic gradients, and customized step length schedule for parametrized quantum gates, i.e. Pauli Rotations. See, for example:

K. Mitarai, M. Negoro, M. Kitagawa, and K. Fujii. (2018). Quantum circuit learning. Phys. Rev. A 98, 032309. https://arxiv.org/abs/1803.00745
Maria Schuld, Ville Bergholm, Christian Gogolin, Josh Izaac, Nathan Killoran. (2019). Evaluating analytic gradients on quantum hardware. Phys. Rev. A 99, 032331. https://arxiv.org/abs/1811.11184

for further details on analytic gradients of parametrized quantum gates.

Gradients are computed “analytically” using the quantum circuit when evaluating the objective function.

Performs Analytical Quantum Gradient Descent (AQGD) with Epochs.

Parameters

maxiter (Union[int, List[int]]) – Maximum number of iterations (full gradient steps)
eta (Union[float, List[float]]) – The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv
tol (float) – Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met.
disp (bool) – Set to True to display convergence messages.
momentum (Union[float, List[float]]) – Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1)
param_tol (float) – Tolerance for change in norm of parameters.
averaging (int) – Length of window over which to average objective values for objective convergence criterion

Raises

AquaError - If the length of maxiter, momentum`, and eta is not the same.

__init__(maxiter=1000, eta=1.0, tol=1e-06, disp=False, momentum=0.25, param_tol=1e-06, averaging=10)

Performs Analytical Quantum Gradient Descent (AQGD) with Epochs.

Parameters

maxiter (Union[int, List[int]]) – Maximum number of iterations (full gradient steps)
eta (Union[float, List[float]]) – The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv
tol (float) – Tolerance for change in windowed average of objective values. Convergence occurs when either objective tolerance is met OR parameter tolerance is met.
disp (bool) – Set to True to display convergence messages.
momentum (Union[float, List[float]]) – Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1)
param_tol (float) – Tolerance for change in norm of parameters.
averaging (int) – Length of window over which to average objective values for objective convergence criterion

Raises

AquaError - If the length of maxiter, momentum`, and eta is not the same.

Methods


`__init__`([maxiter, eta, tol, disp, …])	Performs Analytical Quantum Gradient Descent (AQGD) with Epochs.
`get_support_level`()	Support level dictionary
`gradient_num_diff`(x_center, f, epsilon[, …])	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`optimize`(num_vars, objective_function[, …])	Perform optimization.
`print_options`()	Print algorithm-specific options.
`set_max_evals_grouped`(limit)	Set max evals grouped
`set_options`(**kwargs)	Sets or updates values in the options dictionary.
`wrap_function`(function, args)	Wrap the function to implicitly inject the args at the call of the function.


`bounds_support_level`	Returns bounds support level
`gradient_support_level`	Returns gradient support level
`initial_point_support_level`	Returns initial point support level
`is_bounds_ignored`	Returns is bounds ignored
`is_bounds_required`	Returns is bounds required
`is_bounds_supported`	Returns is bounds supported
`is_gradient_ignored`	Returns is gradient ignored
`is_gradient_required`	Returns is gradient required
`is_gradient_supported`	Returns is gradient supported
`is_initial_point_ignored`	Returns is initial point ignored
`is_initial_point_required`	Returns is initial point required
`is_initial_point_supported`	Returns is initial point supported
`setting`	Return setting

Returns bounds support level

get_support_level()

Support level dictionary

Returns

gradient, bounds and initial point

support information that is ignored/required.

Return type

Dict[str, int]

static gradient_num_diff(x_center, f, epsilon, max_evals_grouped=1)

We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.

Parameters

Returns

the gradient computed

Return type

grad

Returns gradient support level

Returns initial point support level

Returns is bounds ignored

Returns is bounds required

Returns is bounds supported

Returns is gradient ignored

Returns is gradient required

Returns is gradient supported

Returns is initial point ignored

Returns is initial point required

Returns is initial point supported

optimize(num_vars, objective_function, gradient_function=None, variable_bounds=None, initial_point=None)

Perform optimization.

Parameters

num_vars (int) – Number of parameters to be optimized.
objective_function (callable) – A function that computes the objective function.
gradient_function (callable) – A function that computes the gradient of the objective function, or None if not available.
variable_bounds (list[(float, float)]) – List of variable bounds, given as pairs (lower, upper). None means unbounded.
initial_point (numpy.ndarray[float]) – Initial point.

Return type

Tuple [ndarray, float, int]

Returns

point, value, nfev

point: is a 1D numpy.ndarray[float] containing the solution value: is a float with the objective function value nfev: number of objective function calls made if available or None

Raises

ValueError - invalid input

print_options()

Print algorithm-specific options.

set_max_evals_grouped(limit)

Set max evals grouped

set_options(**kwargs)

Sets or updates values in the options dictionary.

The options dictionary may be used internally by a given optimizer to pass additional optional values for the underlying optimizer/optimization function used. The options dictionary may be initially populated with a set of key/values when the given optimizer is constructed.

Parameters

kwargs (dict) - options, given as name=value.

Return setting

static wrap_function(function, args)

Wrap the function to implicitly inject the args at the call of the function.

Parameters

Returns

wrapper

Return type

function_wrapper

Was this page helpful?

Report a bug or request content on GitHub.