def METHOD_NAME(self):
    """
    Check whether sample distribution approximates the probability vector in the limit of
    large number of shots
    NOTE: if the assertion error still keeps failing for no apparent reason, try changing
          ``decimal`` argument in `np.testing.assert_array_almost_equal` to something lower!
    """
    # testing a large number of shots!
    nshots = 500000
    # random 6-qubit Hamiltonian with betas, gammas = 0,0 => only hadamards
    reg = [0, 1, 2, 3, 4, 5]
    p = 1
    betas, gammas = ([0], [0])
    cost_hamiltonian = random_classical_hamiltonian(reg)
    n_qubits = cost_hamiltonian.n_qubits
    mixer_hamiltonian = X_mixer_hamiltonian(n_qubits)
    qaoa_descriptor = QAOADescriptor(cost_hamiltonian, mixer_hamiltonian, p)
    variational_params_std = QAOAVariationalStandardParams(
        qaoa_descriptor, betas, gammas
    )
    backend_vectorized = get_qaoa_backend(
        qaoa_descriptor, DeviceLocal("vectorized")
    )
    # wf_vec = backend_vectorized.wavefunction(variational_params_std)
    prob_wf_vec = np.array(
        list(backend_vectorized.probability_dict(variational_params_std).values()),
        dtype=float,
    )
    samples_vec = backend_vectorized.sample_from_wavefunction(
        variational_params_std, n_samples=nshots
    )
    samples_dict_vec = {bin(x)[2:].zfill(n_qubits): 0 for x in range(2**n_qubits)}
    for shot_result in samples_vec:
        samples_dict_vec[shot_result] += 1 / nshots
    samples_prob_vec = np.array(list(samples_dict_vec.values()), dtype=float)
    np.testing.assert_array_almost_equal(prob_wf_vec, samples_prob_vec, decimal=3)