pauli twirling callibration of expectation_estimation_shadow needs continue

[bdg221]

In shadows/classical_postprocessing.py the expectation_estimation_shadow code includes a section that uses pauli_twirling_calibration. If the f_val returned however is None, then the value, 0.0, is added to the means List. However, the code will continue to execute and a second (invalid) value, np.sum(product) / len(idxes), is added to the means List. Only a single mean should be added to the means List object per split (each loop of the for loop.)

for idxes in group_idxes:
if len(
np.nonzero(np.all(u_lists_shadow[idxes] == target_obs, axis=1))[0]
):
product = (-1) ** np.sum(
b_lists_shadow[
np.nonzero(
np.all(u_lists_shadow[idxes] == target_obs, axis=1)
)
].astype(int),
axis=1,
)

        if pauli_twirling_calibration:
            if f_est is None:
                raise ValueError(
                    "estimation of Pauli fidelity must be provided for"
                    "Pauli twirling calibration."
                )

            b = create_string(num_qubits, target_locs)
            f_val = f_est.get(b, None)
            if f_val is None:
                means.append(0.0)
            else:
                # product becomes an array of snapshots expectation values
                # witch satisfy condition (1) and (2)
                product = (1.0 / f_val) * product
        else:
            product = 3 ** (len(target_locs)) * product

        # append the mean of the product in each split
        means.append(np.sum(product) / len(idxes))
    else:
        means.append(0.0)

The solution would be to add "continue" after "means.append(0.0) to have the code properly move on to the next split.

[bdg221]

@natestemen please assign to me and I will get this included for Milestone 0.32.0