def test_majority_vote_classical_prob(self):
bits = [1, 2, 3]
sdm = SparseDM(bits)
sdm.hadamard(1)
sdm.hadamard(2)
sdm.hadamard(3)
p0 = 0.42
sdm.classical_probability = p0
result = {b: 0 for b in bits}
p = sdm.majority_vote(result)
assert np.allclose(p, 0.5 * p0)
sdm.hadamard(3)
p = sdm.majority_vote(result)
assert np.allclose(p, 0.75 * p0)
p = sdm.majority_vote({1: 0, 2: 1, 3: 0})
assert np.allclose(p, 0.75 * p0)
def test_multiple_measurement_classical_prob(self):
sdm = SparseDM(3)
sdm.hadamard(0)
sdm.hadamard(1)
sdm.classical_probability = 0.7
r = sdm.peak_multiple_measurements([0, 1, 2])
total_prob = sum(prob for outcome, prob in r)
assert total_prob == sdm.trace()
