def test_max_weight_state_4():
states = [
plusX(1) * minusZ(0),
plusX(0),
minusZ(1),
]
assert _max_weight_state(states) is None
def test_max_weight_state_3():
states = [
plusX(0) * minusZ(1),
plusX(0),
minusZ(1),
]
assert _max_weight_state(states) == states[0]
def test_max_tpb_overlap_1():
tomo_expt_settings = [ExperimentSetting(sZ(1) * sX(0), sY(2) * sY(1)),
ExperimentSetting(sX(2) * sZ(1), sY(2) * sZ(0))]
tomo_expt_program = Program(H(0), H(1), H(2))
tomo_expt_qubits = [0, 1, 2]
tomo_expt = TomographyExperiment(tomo_expt_settings, tomo_expt_program, tomo_expt_qubits)
expected_dict = {
ExperimentSetting(plusX(0) * plusZ(1) * plusX(2), sZ(0) * sY(1) * sY(2)): [
ExperimentSetting(plusZ(1) * plusX(0), sY(2) * sY(1)),
ExperimentSetting(plusX(2) * plusZ(1), sY(2) * sZ(0))
]
}
assert expected_dict == _max_tpb_overlap(tomo_expt)
def test_experiment_result():
er = ExperimentResult(
setting=ExperimentSetting(plusX(0), sZ(0)),
expectation=0.9,
stddev=0.05,
total_counts=100,
)
assert str(er) == 'X0_0→(1+0j)*Z0: 0.9 +- 0.05'
def test_expt_settings_diagonal_in_tpb():
def _expt_settings_diagonal_in_tpb(es1: ExperimentSetting, es2: ExperimentSetting):
"""
Extends the concept of being diagonal in the same tpb to ExperimentSettings, by
determining if the pairs of in_states and out_operators are separately diagonal in the same
tpb
"""
max_weight_in = _max_weight_state([es1.in_state, es2.in_state])
max_weight_out = _max_weight_operator([es1.out_operator, es2.out_operator])
return max_weight_in is not None and max_weight_out is not None
expt_setting1 = ExperimentSetting(plusZ(1) * plusX(0), sY(1) * sZ(0))
expt_setting2 = ExperimentSetting(plusY(2) * plusZ(1), sZ(2) * sY(1))
assert _expt_settings_diagonal_in_tpb(expt_setting1, expt_setting2)
expt_setting3 = ExperimentSetting(plusX(2) * plusZ(1), sZ(2) * sY(1))
expt_setting4 = ExperimentSetting(plusY(2) * plusZ(1), sX(2) * sY(1))
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting3)
assert not _expt_settings_diagonal_in_tpb(expt_setting2, expt_setting4)
def test_lifted_state_operator_backwards_qubits():
xz_state = plusX(5) * minusZ(6)
plus = np.array([1, 1]) / np.sqrt(2)
plus = plus[:, np.newaxis]
proj_plus = plus @ plus.conj().T
assert proj_plus.shape == (2, 2)
one = np.array([0, 1])
one = one[:, np.newaxis]
proj_one = one @ one.conj().T
assert proj_one.shape == (2, 2)
np.testing.assert_allclose(
np.kron(proj_plus, proj_one),
lifted_state_operator(xz_state, qubits=[6, 5]),
)