qiskit.algorithms.optimizers.SPSA.calibrate¶
-
static
SPSA.calibrate(loss, initial_point, c=0.2, stability_constant=0, target_magnitude=None, alpha=0.602, gamma=0.101, modelspace=False)[source]¶ Calibrate SPSA parameters with a powerseries as learning rate and perturbation coeffs.
The powerseries are:
\[a_k = \frac{a}{(A + k + 1)^\alpha}, c_k = \frac{c}{(k + 1)^\gamma}\]- Parameters
loss (
Callable[[ndarray],float]) – The loss function.initial_point (
ndarray) – The initial guess of the iteration.c (
float) – The initial perturbation magnitude.stability_constant (
float) – The value of A.target_magnitude (
Optional[float]) – The target magnitude for the first update step, defaults to \(2\pi / 10\).alpha (
float) – The exponent of the learning rate powerseries.gamma (
float) – The exponent of the perturbation powerseries.modelspace (
bool) – Whether the target magnitude is the difference of parameter values or function values (= model space).
- Returns
- A tuple of powerseries generators, the first one for the
learning rate and the second one for the perturbation.
- Return type
tuple(generator, generator)