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AQGD

class AQGD(maxiter=1000, eta=1.0, tol=1e-06, momentum=0.25, param_tol=1e-06, averaging=10)[código fonte]

Bases: 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 parameterized 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 parameterized quantum gates.

Gradients are computed «analytically» using the quantum circuit when evaluating the objective function.

Performs Analytical Quantum Gradient Descent (AQGD) with Epochs.

Parâmetros
  • 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.

  • 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

Levanta

AlgorithmError – 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.

minimize

Minimize the scalar function.

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

settings
Tipo de retorno

Dict[str, Any]