def test_apply(self, n_wires):
"""Test if the apply_controlled_Q performs the correct transformation by reconstructing the
unitary and comparing against the one provided in make_Q. Random unitaries are chosen for
a_mat and r_mat."""
n_all_wires = n_wires + 1
wires = range(n_wires)
target_wire = n_wires - 1
control_wire = n_wires
a_mat = unitary_group.rvs(2**(n_wires - 1), random_state=1967)
r_mat = unitary_group.rvs(2**n_wires, random_state=1967)
q_mat = make_Q(a_mat, r_mat)
def fn():
qml.QubitUnitary(a_mat, wires=wires[:-1])
qml.QubitUnitary(r_mat, wires=wires)
circ = apply_controlled_Q(fn,
wires=wires,
target_wire=target_wire,
control_wire=control_wire,
work_wires=None)
u = get_unitary(circ, n_all_wires)
circ = lambda: qml.ControlledQubitUnitary(
q_mat, wires=wires, control_wires=control_wire)
u_ideal = get_unitary(circ, n_all_wires)
assert np.allclose(u_ideal, u)
def test_apply(self, n_wires):
"""Test if the quantum_monte_carlo performs the correct transformation by reconstructing the
unitary and comparing against the one provided in the QuantumPhaseEstimation template.
Random unitaries are chosen for a_mat and r_mat."""
n_all_wires = 2 * n_wires
wires = range(n_wires)
target_wire = n_wires - 1
estimation_wires = range(n_wires, 2 * n_wires)
a_mat = unitary_group.rvs(2**(n_wires - 1), random_state=1967)
r_mat = unitary_group.rvs(2**n_wires, random_state=1967)
q_mat = make_Q(a_mat, r_mat)
def fn():
qml.QubitUnitary(a_mat, wires=wires[:-1])
qml.QubitUnitary(r_mat, wires=wires)
circ = quantum_monte_carlo(fn,
wires=wires,
target_wire=target_wire,
estimation_wires=estimation_wires)
u = get_unitary(circ, n_all_wires)
def circ_ideal():
fn()
qml.templates.QuantumPhaseEstimation(
q_mat, target_wires=wires, estimation_wires=estimation_wires)
u_ideal = get_unitary(circ_ideal, n_all_wires)
assert np.allclose(u_ideal, u)
def test_expected_circuit(self):
"""Test if the circuit applied when using the QMC template is the same as the expected
circuit for a fixed example"""
p = np.ones(4) / 4
target_wires, estimation_wires = Wires(range(3)), Wires(range(3, 5))
with qml.tape.QuantumTape() as tape:
QuantumMonteCarlo(p, self.func, target_wires, estimation_wires)
queue_before_qpe = tape.queue[:2]
queue_after_qpe = tape.queue[2:]
A = probs_to_unitary(p)
R = func_to_unitary(self.func, 4)
assert len(queue_before_qpe) == 2
assert queue_before_qpe[0].name == "QubitUnitary"
assert queue_before_qpe[1].name == "QubitUnitary"
assert np.allclose(queue_before_qpe[0].matrix, A)
assert np.allclose(queue_before_qpe[1].matrix, R)
assert queue_before_qpe[0].wires == target_wires[:-1]
assert queue_before_qpe[1].wires == target_wires
Q = make_Q(A, R)
with qml.tape.QuantumTape() as qpe_tape:
qml.templates.QuantumPhaseEstimation(Q, target_wires, estimation_wires)
assert len(queue_after_qpe) == len(qpe_tape.queue)
assert all(o1.name == o2.name for o1, o2 in zip(queue_after_qpe, qpe_tape.queue))
assert all(
np.allclose(o1.matrix, o2.matrix) for o1, o2 in zip(queue_after_qpe, qpe_tape.queue)
)
assert all(o1.wires == o2.wires for o1, o2 in zip(queue_after_qpe, qpe_tape.queue))
def test_expected_circuit(self):
"""Test if the circuit applied when using the QMC template is the same as the expected
circuit for a fixed example"""
p = np.ones(4) / 4
target_wires, estimation_wires = Wires(range(3)), Wires(range(3, 5))
op = QuantumMonteCarlo(p, self.func, target_wires, estimation_wires)
tape = op.expand()
# Do expansion in two steps to avoid also decomposing the first QubitUnitary
queue_before_qpe = tape.operations[:2]
# 2-qubit decomposition has 10 operations, and after is a 3-qubit gate so start at 11
queue_after_qpe = tape.expand().operations[11:]
A = probs_to_unitary(p)
R = func_to_unitary(self.func, 4)
assert len(queue_before_qpe) == 2
assert queue_before_qpe[0].name == "QubitUnitary"
assert queue_before_qpe[1].name == "QubitUnitary"
assert np.allclose(queue_before_qpe[0].matrix, A)
assert np.allclose(queue_before_qpe[1].matrix, R)
assert queue_before_qpe[0].wires == target_wires[:-1]
assert queue_before_qpe[1].wires == target_wires
Q = make_Q(A, R)
with qml.tape.QuantumTape() as qpe_tape:
qml.QuantumPhaseEstimation(Q, target_wires, estimation_wires)
qpe_tape = qpe_tape.expand()
assert len(queue_after_qpe) == len(qpe_tape.operations)
assert all(o1.name == o2.name
for o1, o2 in zip(queue_after_qpe, qpe_tape.operations))
assert all(
np.allclose(o1.matrix, o2.matrix)
for o1, o2 in zip(queue_after_qpe, qpe_tape.operations))
assert all(o1.wires == o2.wires
for o1, o2 in zip(queue_after_qpe, qpe_tape.operations))
def test_Q():
"""Test for the make_Q function using a fixed example"""
A = np.array([
[0.85358423 - 0.32239299j, -0.12753659 + 0.38883306j],
[0.39148136 - 0.11915985j, 0.34064316 - 0.84646648j],
])
R = np.array([
[
0.45885289 + 0.03972856j,
0.2798685 - 0.05981098j,
0.64514642 - 0.51555038j,
0.11015177 - 0.10877695j,
],
[
0.19407005 - 0.35483005j,
0.29756077 + 0.80153453j,
-0.19147104 + 0.0507968j,
0.15553799 - 0.20493631j,
],
[
0.35083011 - 0.20807392j,
-0.27602911 - 0.13934692j,
0.11874165 + 0.34532609j,
-0.45945242 - 0.62734969j,
],
[
-0.11379919 - 0.66706921j,
-0.21120956 - 0.2165113j,
0.30133006 + 0.23367271j,
0.54593491 + 0.08446372j,
],
])
Q_expected = np.array([
[
-0.46513201 - 1.38777878e-17j,
-0.13035515 - 2.23341802e-01j,
-0.74047856 + 7.08652160e-02j,
-0.0990036 - 3.91977176e-01j,
],
[
0.13035515 - 2.23341802e-01j,
0.46494302 + 0.00000000e00j,
0.05507901 - 1.19182067e-01j,
-0.80370146 - 2.31904873e-01j,
],
[
-0.74047856 - 7.08652160e-02j,
-0.05507901 - 1.19182067e-01j,
0.62233412 - 2.77555756e-17j,
-0.0310774 - 2.02894077e-01j,
],
[
0.0990036 - 3.91977176e-01j,
-0.80370146 + 2.31904873e-01j,
0.0310774 - 2.02894077e-01j,
-0.30774091 + 2.77555756e-17j,
],
])
Q = make_Q(A, R)
assert np.allclose(Q, Q_expected)
