AQGD

class AQGD(maxiter=1000, eta=3.0, tol=1e-06, disp=False, momentum=0.25)

Analytic Quantum Gradient Descent (AQGD) optimizer.

Performs gradient descent optimization with a momentum term and analytic gradients 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.

Parameters

• maxiter (int) – Maximum number of iterations, each iteration evaluation gradient.

• eta (float) – The coefficient of the gradient update. Increasing this value results in larger step sizes: param = previous_param - eta * deriv

• tol (float) – The convergence criteria that must be reached before stopping. Optimization stops when: absolute(loss - previous_loss) < tol

• disp (bool) – Set to True to display convergence messages.

• momentum (float) – Bias towards the previous gradient momentum in current update. Must be within the bounds: [0,1)

Attributes

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

Methods

AQGD.converged(objval[, n])	Determines if the objective function has converged by finding the difference between the current value and the previous n values.
AQGD.deriv(j, params, obj)	Obtains the analytical quantum derivative of the objective function with respect to the jth parameter.
AQGD.get_support_level()	Return support level dictionary
AQGD.gradient_num_diff(x_center, f, epsilon)	We compute the gradient with the numeric differentiation in the parallel way, around the point x_center.
AQGD.optimize(num_vars, objective_function)	Perform optimization.
AQGD.print_options()	Print algorithm-specific options.
AQGD.set_max_evals_grouped(limit)	Set max evals grouped
AQGD.set_options(**kwargs)	Sets or updates values in the options dictionary.
AQGD.update(j, params, deriv, mprev)	Updates the jth parameter based on the derivative and previous momentum
AQGD.wrap_function(function, args)	Wrap the function to implicitly inject the args at the call of the function.