def test_repr():
gate = ccb.BayesianNetworkGate([('q0', 0.0), ('q1', None)], [('q1', ('q0',), [0.0, 0.0])])
assert repr(gate) == (
"cirq.BayesianNetworkGate(init_probs=[('q0', 0.0), ('q1', None)],"
+ " arc_probs=[('q1', ('q0',), [0.0, 0.0])])"
)
def test_incorrect_constructor():
# Success building.
ccb.BayesianNetworkGate([('q0', 0.0), ('q1', None)], [('q1', ('q0',), [0.0, 0.0])])
with pytest.raises(ValueError, match='Initial prob should be between 0 and 1.'):
ccb.BayesianNetworkGate([('q0', 2016.0913), ('q1', None)], [('q1', ('q0',), [0.0, 0.0])])
# This is an easy mistake where the tuple for q0 doesn't have the comma at the end.
with pytest.raises(ValueError, match='Conditional prob params must be a tuple.'):
ccb.BayesianNetworkGate([('q0', 0.0), ('q1', None)], [('q1', ('q0'), [0.0, 0.0])])
with pytest.raises(ValueError, match='Incorrect number of conditional probs.'):
ccb.BayesianNetworkGate([('q0', 0.0), ('q1', None)], [('q1', ('q0',), [0.0])])
with pytest.raises(ValueError, match='Conditional prob should be between 0 and 1.'):
ccb.BayesianNetworkGate([('q0', 0.0), ('q1', None)], [('q1', ('q0',), [2016.0913, 0.0])])
def test_repro_figure_10_of_paper():
# We try to create the network of figure 10 and check that the probabilities are the same as
# the ones in table 10 of https://arxiv.org/abs/2004.14803.
ir = cirq.NamedQubit('q4_IR')
oi = cirq.NamedQubit('q3_OI')
sm = cirq.NamedQubit('q2_SM')
sp = cirq.NamedQubit('q0_SP')
gate = ccb.BayesianNetworkGate(
[('ir', 0.25), ('oi', 0.4), ('sm', None), ('sp', None)],
[('sm', ('ir',), [0.7, 0.2]), ('sp', ('sm', 'oi'), [0.1, 0.5, 0.6, 0.8])],
)
qubits = [sp, sm, oi, ir]
circuit = cirq.Circuit(cirq.decompose_once(gate.on(*qubits)))
result = cirq.Simulator().simulate(circuit, qubit_order=qubits, initial_state=0)
probs = np.asarray([abs(x) ** 2 for x in result.state_vector(copy=True)]).reshape(2, 2, 2, 2)
# p(IR = 0) = 0.7500
np.testing.assert_almost_equal(np.sum(probs[0, :, :, :]), 0.7500, decimal=6)
# p(SM = 0) = 0.4250
np.testing.assert_almost_equal(np.sum(probs[:, :, 0, :]), 0.4250, decimal=6)
# p(OI = 0) = 0.6000
np.testing.assert_almost_equal(np.sum(probs[:, 0, :, :]), 0.6000, decimal=6)
# p(SP = 0) = 0.4985
np.testing.assert_almost_equal(np.sum(probs[:, :, :, 0]), 0.4985, decimal=6)
def test_prob_encoding(input_prob):
q = cirq.NamedQubit('q')
gate = ccb.BayesianNetworkGate([('q', input_prob)], [])
circuit = cirq.Circuit(gate.on(q))
phi = (
cirq.Simulator().simulate(circuit, qubit_order=[q], initial_state=0).state_vector(copy=True)
)
actual_probs = [abs(x) ** 2 for x in phi]
np.testing.assert_almost_equal(actual_probs[1], input_prob, decimal=4)
def test_initial_probs(p0, p1, p2, expected_probs, decompose):
q0, q1, q2 = cirq.LineQubit.range(3)
gate = ccb.BayesianNetworkGate([('q0', p0), ('q1', p1), ('q2', p2)], [])
if decompose:
circuit = cirq.Circuit(cirq.decompose(gate.on(q0, q1, q2)))
else:
circuit = cirq.Circuit(gate.on(q0, q1, q2))
result = cirq.Simulator().simulate(circuit, qubit_order=[q0, q1, q2], initial_state=0)
actual_probs = [abs(x) ** 2 for x in result.state_vector(copy=True)]
np.testing.assert_allclose(actual_probs, expected_probs, atol=1e-6)
def test_arc_probs(input_prob_q0, input_prob_q1, expected_prob_q2, decompose):
q0, q1, q2 = cirq.LineQubit.range(3)
gate = ccb.BayesianNetworkGate(
[('q0', input_prob_q0), ('q1', input_prob_q1), ('q2', None)],
[('q2', ('q0', 'q1'), [0.1, 0.2, 0.3, 0.4])],
)
if decompose:
circuit = cirq.Circuit(cirq.decompose(gate.on(q0, q1, q2)))
else:
circuit = cirq.Circuit(gate.on(q0, q1, q2))
result = cirq.Simulator().simulate(circuit, qubit_order=[q0, q1, q2], initial_state=0)
probs = [abs(x) ** 2 for x in result.state_vector(copy=True)]
actual_prob_q2_is_one = sum(probs[1::2])
np.testing.assert_almost_equal(actual_prob_q2_is_one, expected_prob_q2, decimal=4)
def test_basic_properties():
gate = ccb.BayesianNetworkGate([('q0', None), ('q1', None), ('q2', None)], [])
assert gate._has_unitary_()
assert gate._qid_shape_() == (2, 2, 2)