cfa828f6349317ce50a610cd31bb3dcf5e5ea211,qucumber/utils/training_statistics.py,,fidelity,#Any#Any#Any#,22

Before Change

    Z = nn_state.compute_normalization(space)
    F = torch.tensor([0.0, 0.0], dtype=torch.double, device=nn_state.device)
    target_psi = target_psi.to(nn_state.device)
    for i in range(len(space)):
        psi = nn_state.psi(space[i]) / Z.sqrt()
        F[0] += target_psi[0, i] * psi[0] + target_psi[1, i] * psi[1]
        F[1] += target_psi[0, i] * psi[1] - target_psi[1, i] * psi[0]
    return cplx.norm_sqr(F)


def rotate_psi(nn_state, basis, space, unitaries, psi=None):

After Change

    target_psi = target_psi.to(nn_state.device)
    psi = nn_state.psi(space) / Z.sqrt()
    F = cplx.inner_prod(target_psi, psi)
    return cplx.absolute_value(F).pow_(2).item()


def rotate_psi(nn_state, basis, space, unitaries, psi=None):
    rA function that rotates the reconstructed wavefunction to a different

Instances