n_errors.extend(n_error_vec)
for i in range(1, len(sizes)):
n_qubits = sizes[i]**2
n_error_vec = removedup(vround(error_probabilities * n_qubits))
n_errors.extend(n_error_vec[n_error_vec > n_errors[-1]])
biasstr_list = ['X', 'Y', 'Z']
for biasstr in biasstr_list:
timestr = time.strftime("%Y%m%d-%H%M%S ") #record current date and time
dirname = "./data/" + timestr + code_name + '_bias=' + biasstr
os.mkdir(dirname)
for bias in bias_list:
error_model = BiasedDepolarizingErrorModel(bias, biasstr)
chi_val = 10
decoder = _rotatedplanarmpsdecoder_def.RotatedPlanarMPSDecoder_def(
chi=chi_val)
# print run parameters
print('codes_and_size:', [code.label for code in codes_and_size])
print('Error model:', error_model.label)
print('Decoder:', decoder.label)
print('Error probabilities:', error_probabilities)
print('Maximum runs:', max_runs)
pL_list_rand = np.zeros(
(len(codes_and_size), realizations, len(error_probabilities)))
std_list_rand = np.zeros(
(len(codes_and_size), realizations, len(error_probabilities)))
def test_planar_rmps_decoder_cosets_probability_pair_optimisation(shape, mode):
code = PlanarCode(*shape)
decoder = PlanarRMPSDecoder(mode=mode)
# probabilities
prob_dist = BiasedDepolarizingErrorModel(
bias=10).probability_distribution(0.1)
# coset probabilities for null Pauli
coset_i_ps, _ = decoder._coset_probabilities(prob_dist, code.new_pauli())
# X
coset_x_ps, _ = decoder._coset_probabilities(prob_dist,
code.new_pauli().logical_x())
# expect Pr(iIG) ~= Pr(xXG)
assert _is_close(
coset_i_ps[0], coset_x_ps[1], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iIG) ~= Pr(xXG)')
# expect Pr(iXG) ~= Pr(xIG)
assert _is_close(
coset_i_ps[1], coset_x_ps[0], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iXG) ~= Pr(xIG)')
# expect Pr(iYG) ~= Pr(xZG)
assert _is_close(
coset_i_ps[2], coset_x_ps[3], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iYG) ~= Pr(xZG)')
# expect Pr(iZG) ~= Pr(xYG)
assert _is_close(
coset_i_ps[3], coset_x_ps[2], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iZG) ~= Pr(xYG)')
# Y
coset_y_ps, _ = decoder._coset_probabilities(
prob_dist,
code.new_pauli().logical_x().logical_z())
# expect Pr(iIG) ~= Pr(yYG)
assert _is_close(
coset_i_ps[0], coset_y_ps[2], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iIG) ~= Pr(yYG)')
# expect Pr(iXG) ~= Pr(yZG)
assert _is_close(
coset_i_ps[1], coset_y_ps[3], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iXG) ~= Pr(yZG)')
# expect Pr(iYG) ~= Pr(yIG)
assert _is_close(
coset_i_ps[2], coset_y_ps[0], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iYG) ~= Pr(yIG)')
# expect Pr(iZG) ~= Pr(yXG)
assert _is_close(
coset_i_ps[3], coset_y_ps[1], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iZG) ~= Pr(yXG)')
# Z
coset_z_ps, _ = decoder._coset_probabilities(prob_dist,
code.new_pauli().logical_z())
# expect Pr(iIG) ~= Pr(zZG)
assert _is_close(
coset_i_ps[0], coset_z_ps[3], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iIG) ~= Pr(zZG)')
# expect Pr(iXG) ~= Pr(zYG)
assert _is_close(
coset_i_ps[1], coset_z_ps[2], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iXG) ~= Pr(zYG)')
# expect Pr(iYG) ~= Pr(zXG)
assert _is_close(
coset_i_ps[2], coset_z_ps[1], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iYG) ~= Pr(zXG)')
# expect Pr(iZG) ~= Pr(zIG)
assert _is_close(
coset_i_ps[3], coset_z_ps[0], rtol=1e-15,
atol=0), ('Coset probabilites do not satisfy Pr(iZG) ~= Pr(zIG)')
def test_biased_depolarizing_error_model_invalid_parameters(bias, axis):
with pytest.raises((ValueError, TypeError), match=r"^BiasedDepolarizingErrorModel") as exc_info:
BiasedDepolarizingErrorModel(bias, axis)
print(exc_info)
def label(self):
return 'fixed'
@pytest.mark.parametrize('code, error_model, decoder', [
# each code with each valid decoder
(Color666Code(5), DepolarizingErrorModel(), Color666MPSDecoder(chi=8)),
(FiveQubitCode(), DepolarizingErrorModel(), NaiveDecoder()),
(PlanarCode(5, 5), DepolarizingErrorModel(), PlanarCMWPMDecoder()),
(PlanarCode(5, 5), DepolarizingErrorModel(), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), DepolarizingErrorModel(), PlanarMWPMDecoder()),
(PlanarCode(5, 5), DepolarizingErrorModel(), PlanarRMPSDecoder(chi=6)),
(PlanarCode(4, 5), BitPhaseFlipErrorModel(), PlanarYDecoder()),
(RotatedPlanarCode(7, 7), DepolarizingErrorModel(), RotatedPlanarMPSDecoder(chi=8)),
(RotatedPlanarCode(7, 7), DepolarizingErrorModel(), RotatedPlanarRMPSDecoder(chi=8)),
(RotatedPlanarCode(7, 7), BiasedDepolarizingErrorModel(100), RotatedPlanarSMWPMDecoder()),
(RotatedToricCode(6, 6), BiasedDepolarizingErrorModel(100), RotatedToricSMWPMDecoder()),
(SteaneCode(), DepolarizingErrorModel(), NaiveDecoder()),
(ToricCode(5, 5), DepolarizingErrorModel(), ToricMWPMDecoder()),
# each generic noise model
(PlanarCode(5, 5), BiasedDepolarizingErrorModel(10), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), BiasedYXErrorModel(10), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), BitFlipErrorModel(), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), BitPhaseFlipErrorModel(), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), CenterSliceErrorModel((0.2, 0.8, 0), 0.5), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), DepolarizingErrorModel(), PlanarMPSDecoder(chi=6)),
(PlanarCode(5, 5), PhaseFlipErrorModel(), PlanarMPSDecoder(chi=6)),
])
def test_run_once(code, error_model, decoder):
error_probability = 0.15
data = app.run_once(code, error_model, decoder, error_probability) # no error raised
def test_biased_depolarizing_error_model_valid_parameters(bias, axis):
BiasedDepolarizingErrorModel(bias, axis) # no error raised
