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.

매개변수

• 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

예외

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.

매개변수

• 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

예외

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.

Attributes

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

property bounds_support_level

Returns bounds support level

get_support_level()

Support level dictionary

반환값

gradient, bounds and initial point

support information that is ignored/required.

반환 형식

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.

매개변수

• x_center (ndarray) – point around which we compute the gradient

• f (func) – the function of which the gradient is to be computed.

• epsilon (float) – the epsilon used in the numeric differentiation.

• max_evals_grouped (int) – max evals grouped

반환값

the gradient computed

반환 형식

grad

property gradient_support_level

Returns gradient support level

property initial_point_support_level

Returns initial point support level

property is_bounds_ignored

Returns is bounds ignored

property is_bounds_required

Returns is bounds required

property is_bounds_supported

Returns is bounds supported

property is_gradient_ignored

Returns is gradient ignored

property is_gradient_required

Returns is gradient required

property is_gradient_supported

Returns is gradient supported

property is_initial_point_ignored

Returns is initial point ignored

property is_initial_point_required

Returns is initial point required

property is_initial_point_supported

Returns is initial point supported

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

Perform optimization.

매개변수

• 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.

반환 형식

Tuple[ndarray, float, int]

반환값

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

예외

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.

매개변수

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

property setting

Return setting

static wrap_function(function, args)

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

매개변수

• function (func) – the target function

• args (tuple) – the args to be injected

반환값

wrapper

반환 형식

function_wrapper