def test_set_bit():
sdm = SparseDM(10)
sdm.set_bit(0, 1)
assert sdm.classical[0] == 1
sdm.hadamard(0)
sdm.hadamard(0)
def test_multiple_measurement_hadamard_on_classical(self):
sdm = SparseDM(2)
sdm.hadamard(0)
meas = sdm.peak_multiple_measurements([0, 1])
assert len(meas) == 2
assert meas == [({0: 0, 1: 0}, 0.5), ({0: 1, 1: 0}, 0.5)]
def test_renormalize():
sdm = SparseDM(2)
sdm.hadamard(0)
sdm.project_measurement(0, 1)
assert np.allclose(sdm.trace(), 0.5)
sdm.renormalize()
assert np.allclose(sdm.trace(), 1)
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()
def test_multiple_does_not_change(self):
sdm = SparseDM(3)
sdm.hadamard(0)
sdm.hadamard(1)
sdm.ensure_dense(0)
sdm.ensure_dense(1)
sdm.ensure_dense(2)
before = sdm.full_dm.to_array()
assert before.shape == (8, 8)
sdm.peak_multiple_measurements([0, 1, 2])
assert np.allclose(before, sdm.full_dm.to_array())
def test_meas_on_hadamard():
sdm = SparseDM(1)
sdm.hadamard(0)
p0, p1 = sdm.peak_measurement(0)
assert p0 == 0.5
assert p1 == 0.5
sdm.project_measurement(0, 1)
print(sdm.full_dm.to_array())
assert len(sdm.classical) == 1
assert sdm.full_dm.no_qubits == 0
assert sdm.classical[0] == 1
assert np.allclose(sdm.trace(), 0.5)
def test_multiple_measurement_hadamard_order2_regression(self):
sdm = SparseDM(3)
sdm.hadamard(0)
sdm.hadamard(1)
sdm.ensure_dense(0)
sdm.ensure_dense(1)
sdm.ensure_dense(2)
meas = sdm.peak_multiple_measurements([0, 1, 2])
assert len(meas) == 8
for state, p in meas:
print(meas)
if state[2] == 0:
assert np.allclose(p, 0.25)
else:
assert np.allclose(p, 0)
def test_multiple_measurement_hadamard_order1(self):
sdm = SparseDM(3)
sdm.hadamard(0)
sdm.hadamard(2)
sdm.ensure_dense(0)
sdm.ensure_dense(1)
sdm.ensure_dense(2)
meas = sdm.peak_multiple_measurements([0, 1, 2])
assert len(meas) == 8
for state, p in meas:
for x in state.values():
assert x in [0, 1]
if state[1] == 0:
assert np.allclose(p, 0.25)
else:
assert np.allclose(p, 0)
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_majority_after_hadamard(self):
bits = [1, 2, 3]
sdm = SparseDM(bits)
sdm.hadamard(1)
sdm.hadamard(2)
sdm.hadamard(3)
p = sdm.majority_vote(bits)
assert np.allclose(p, 0.5)
sdm.hadamard(3)
p = sdm.majority_vote(bits)
assert np.allclose(p, 0.25)
def test_majority_vote_after_hadamard_inverted(self):
bits = [1, 2, 3]
sdm = SparseDM(bits)
sdm.hadamard(1)
sdm.hadamard(2)
sdm.hadamard(3)
result = {b: 0 for b in bits}
p = sdm.majority_vote(result)
assert np.allclose(p, 0.5)
sdm.hadamard(3)
p = sdm.majority_vote(result)
assert np.allclose(p, 0.75)
p = sdm.majority_vote({1: 0, 2: 1, 3: 0})
assert np.allclose(p, 0.75)
def test_ensure_classical_fail_after_hadamard():
sdm = SparseDM(10)
sdm.hadamard(0)
with pytest.raises(ValueError):
sdm.ensure_classical(0)