def test_planar_mps_decoder_small_code_negative_coset_probability(chi, mode):
# parameters
code = PlanarCode(3, 3)
decoder = PlanarMPSDecoder(chi=chi, mode=mode)
error_model = DepolarizingErrorModel()
error_probability = 0.1
# logged run values
error = pt.unpack(["e0048000", 26])
syndrome = pt.bsp(error, code.stabilizers.T)
# debug
print()
print(code.ascii_art(syndrome, code.new_pauli(error)))
# decode
prob_dist = error_model.probability_distribution(error_probability)
any_recovery = decoder.sample_recovery(code, syndrome)
# coset probabilities
coset_ps, recoveries = decoder._coset_probabilities(
prob_dist, any_recovery)
print('chi={}, mode={}, coset_ps={}'.format(chi, mode, coset_ps))
max_coset_p, max_recovery = max(
zip(coset_ps, recoveries),
key=lambda coset_p_recovery: coset_p_recovery[0])
success = np.all(
pt.bsp(max_recovery.to_bsf() ^ error, code.logicals.T) == 0)
print('### success=', success)
assert mp.isfinite(
max_coset_p
) and max_coset_p > 0, 'Max coset probability not as expected'
assert np.all(
np.array(coset_ps) >= 0), 'At least one coset probability is negative'
def test_biased_depolarising_error_model_probability_distribution_bias_half():
for axis in 'XYZ':
bias = 0.5
error_model = BiasedDepolarizingErrorModel(bias, axis)
depolarizing_error_model = DepolarizingErrorModel()
p = 0.1
assert error_model.probability_distribution(p) == depolarizing_error_model.probability_distribution(p), (
'Biased-depolarizing probability (bias=0.5) does not match standard depolarizing probability distribution.')