AQGD¶

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

Bases: qiskit.aqua.components.optimizers.optimizer.Optimizer

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.

Methods

`get_support_level`	Support level dictionary
`gradient_num_diff`	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
`optimize`	Perform optimization.
`print_options`	Print algorithm-specific options.
`set_max_evals_grouped`	Set max evals grouped
`set_options`	Sets or updates values in the options dictionary.
`wrap_function`	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