def test_dot_product_qnodes_qnodes(self, qnodes, interface, tf_support, torch_support):
"""Test that the dot product of qnodes.qnodes can be applied using all interfaces"""
if interface == "torch" and not torch_support:
pytest.skip("Skipped, no torch support")
if interface == "tf" and not tf_support:
pytest.skip("Skipped, no tf support")
qnode1, qnode2 = qnodes
qc1 = qml.QNodeCollection([qnode1, qnode2])
qc2 = qml.QNodeCollection([qnode1, qnode2])
# test the dot product of qnodes, qnodes
cost = qml.dot(qc1, qc2)
params = [0.5643, -0.45]
res = cost(params)
qc1val = qc1(params)
qc2val = qc2(params)
if interface in ("tf", "torch"):
res = res.numpy()
qc1val = qc1val.numpy()
qc2val = qc2val.numpy()
expected = np.dot(qc1val, qc2val)
assert np.all(res == expected)
def test_interface_property(self, interface, tf_support, torch_support):
"""Test that the interface property correctly
resolves interfaces from the internal QNodes"""
if interface == "torch" and not torch_support:
pytest.skip("Skipped, no torch support")
if interface == "tf" and not tf_support:
pytest.skip("Skipped, no tf support")
qc = qml.QNodeCollection()
assert qc.interface is None
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnodes = [
qml.QNode(circuit, dev, interface=interface) for i in range(4)
]
qc = qml.QNodeCollection(qnodes)
if interface == "numpy":
# Note: the "numpy" interface is deprecated, and
# now resolves to "autograd"
interface = "autograd"
assert qc.interface == interface
def test_nested_apply(self, qnodes, interface, tf_support, torch_support, tol):
"""Test that nested apply can be done using all interfaces"""
if interface == "torch" and not torch_support:
pytest.skip("Skipped, no torch support")
if interface == "tf" and not tf_support:
pytest.skip("Skipped, no tf support")
qnode1, qnode2 = qnodes
qc = qml.QNodeCollection([qnode1, qnode2])
if interface == "tf":
sinfn = tf.sin
sfn = tf.reduce_sum
elif interface == "torch":
sinfn = torch.sin
sfn = torch.sum
elif interface == "jax":
sinfn = jnp.sin
sfn = jnp.sum
else:
sinfn = np.sin
sfn = np.sum
cost = qml.collections.apply(sfn, qml.collections.apply(sinfn, qc))
params = [0.5643, -0.45]
res = cost(params)
expected = sfn(sinfn(qc(params)))
assert np.allclose(res, expected, atol=tol, rtol=0)
def test_grad_tf(self, qnodes, skip_if_no_tf_support, parallel):
"""Test correct gradient of the QNodeCollection using
the tf interface"""
qnode1, qnode2 = qnodes
# calculate the gradient of the collection using tf
params = Variable([0.5643, -0.45])
qc = qml.QNodeCollection([qnode1, qnode2])
with tf.GradientTape() as tape:
tape.watch(params)
if parallel:
with pytest.warns(UserWarning):
cost = sum(qc(params, parallel=parallel))
else:
cost = sum(qc(params, parallel=parallel))
# the gradient will be None
res = tape.gradient(cost, params).numpy()
# calculate the gradient of the QNodes individually using tf
params = Variable([0.5643, -0.45])
with tf.GradientTape() as tape:
tape.watch(params)
cost = sum(qnode1(params) + qnode2(params))
expected = tape.gradient(cost, params).numpy()
assert np.all(res == expected)
def test_dot_product_qnodes_tensor(self, qnodes, interface, tf_support, torch_support):
"""Test that the dot product of qnodes.tensor can be applied using all interfaces"""
if interface == "torch" and not torch_support:
pytest.skip("Skipped, no torch support")
if interface == "tf" and not tf_support:
pytest.skip("Skipped, no tf support")
qnode1, _ = qnodes
qc = qml.QNodeCollection([qnode1])
coeffs = [0.5, -0.1]
if interface == "torch":
coeffs = torch.tensor(coeffs, dtype=torch.float64)
if interface == "tf":
coeffs = tf.cast(coeffs, dtype=tf.float64)
# test the dot product of qnodes, tensor
cost = qml.dot(qc, coeffs)
params = [0.5643, -0.45]
res = cost(params)
qcval = qc(params)
if interface in ("tf", "torch"):
res = res.numpy()
qcval = qcval.numpy()
coeffs = coeffs.numpy()
expected = np.dot(qcval, coeffs)
assert np.all(res == expected)
def test_mismatching_interface(self, monkeypatch):
"""Test exception raised if the interfaces don't match"""
dev = qml.device("default.qubit", wires=1)
@qml.qnode(dev, interface=None)
def circuit1(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
@qml.qnode(dev, interface="autograd")
def circuit2(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
qc1 = qml.QNodeCollection([circuit1])
qc2 = qml.QNodeCollection([circuit2])
with pytest.raises(ValueError, match="have non-matching interfaces"):
qml.dot(qc1, qc2)
def test_eval_autograd(self, qnodes):
"""Test correct evaluation of the QNodeCollection using
the Autograd interface"""
qnode1, qnode2 = qnodes
qc = qml.QNodeCollection([qnode1, qnode2])
params = [0.5643, -0.45]
res = qc(params)
expected = np.vstack([qnode1(params), qnode2(params)])
assert np.all(res == expected)
def test_eval_tf(self, qnodes, skip_if_no_tf_support):
"""Test correct evaluation of the QNodeCollection using
the tf interface"""
qnode1, qnode2 = qnodes
qc = qml.QNodeCollection([qnode1, qnode2])
params = [0.5643, -0.45]
res = qc(params).numpy()
expected = np.vstack([qnode1(params), qnode2(params)])
assert np.all(res == expected)
def test_indexing(self):
"""Test that indexing into the QNodeCollection correctly works"""
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnodes = [qml.QNode(circuit, dev) for i in range(4)]
qc = qml.QNodeCollection(qnodes)
assert qc[2] == qnodes[2]
def test_init_with_qnodes(self):
"""Test that a QNode collection can be initialized with QNodes"""
dev = qml.device("default.qubit", wires=1)
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
qnodes = [qml.QNode(circuit, dev) for i in range(4)]
qc = qml.QNodeCollection(qnodes)
assert qc.qnodes == qnodes
assert len(qc) == 4
def test_unknown_interface(self, monkeypatch):
"""Test exception raised if the interface is unknown"""
monkeypatch.setattr(qml.QNodeCollection, "interface", "invalid")
dev = qml.device("default.qubit", wires=1)
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
qnodes = [qml.QNode(circuit, dev) for i in range(4)]
qc = qml.QNodeCollection(qnodes)
with pytest.raises(ValueError, match="Unknown interface invalid"):
qml.sum(qc)
def test_extend_qnodes(self):
"""Test that a list of QNodes is correctly appended"""
qc = qml.QNodeCollection()
assert qc.qnodes == []
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnodes = [qml.QNode(circuit, dev) for i in range(4)]
qc.extend(qnodes)
assert qc.qnodes == [] + qnodes
def test_append_qnode(self):
"""Test that a QNode is correctly appended"""
qc = qml.QNodeCollection()
assert qc.qnodes == []
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnode = qml.QNode(circuit, dev)
qc.append(qnode)
assert qc.qnodes == [qnode]
def test_grad_autograd(self, qnodes):
"""Test correct gradient of the QNodeCollection using
the Autograd interface"""
qnode1, qnode2 = qnodes
params = [0.5643, -0.45]
qc = qml.QNodeCollection([qnode1, qnode2])
cost_qc = lambda params: np.sum(qc(params))
grad_qc = qml.grad(cost_qc, argnum=0)
cost_expected = lambda params: np.sum(qnode1(params) + qnode2(params))
grad_expected = qml.grad(cost_expected, argnum=0)
res = grad_qc(params)
expected = grad_expected(params)
assert np.all(res == expected)
def test_extend_multiple_interface_qnodes(self):
"""Test that an error is returned if QNodes with differing
interfaces are attempted to be added to a QNodeCollection"""
qc = qml.QNodeCollection()
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnodes = [
qml.QNode(circuit, dev, interface="autograd"),
qml.QNode(circuit, dev, interface=None),
]
with pytest.raises(ValueError, match="do not all use the same interface"):
qc.extend(qnodes)
def test_extend_interface_mistmatch(self):
"""Test that an error is returned if QNodes with a differing
interface to the QNode collection are appended"""
qc = qml.QNodeCollection()
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
dev = qml.device("default.qubit", wires=1)
qnode1 = qml.QNode(circuit, dev, interface="autograd")
qnode2 = qml.QNode(circuit, dev, interface=None)
qc.extend([qnode1])
with pytest.raises(ValueError, match="Interface mismatch"):
qc.extend([qnode2])
def test_unknown_interface(self, monkeypatch):
"""Test exception raised if the interface is unknown"""
monkeypatch.setattr(qml.QNodeCollection, "interface", "invalid")
dev = qml.device("default.qubit", wires=1)
@qml.qnode(dev)
def circuit1(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
@qml.qnode(dev)
def circuit2(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
qc = qml.QNodeCollection([circuit1, circuit2])
with pytest.raises(ValueError, match="Unknown interface invalid"):
qml.dot([1, 2], qc)
def test_grad_torch(self, qnodes, skip_if_no_torch_support, parallel):
"""Test correct gradient of the QNodeCollection using
the torch interface"""
qnode1, qnode2 = qnodes
# calculate the gradient of the collection using pytorch
params = torch.autograd.Variable(torch.tensor([0.5643, -0.45]), requires_grad=True)
qc = qml.QNodeCollection([qnode1, qnode2])
cost = torch.sum(qc(params, parallel=parallel))
cost.backward()
res = params.grad.numpy()
# calculate the gradient of the QNodes individually using pytorch
params = torch.autograd.Variable(torch.tensor([0.5643, -0.45]), requires_grad=True)
cost = torch.sum(qnode1(params) + qnode2(params))
cost.backward()
expected = params.grad.numpy()
assert np.all(res == expected)
def test_apply_summation(self, qnodes, interface, tf_support, torch_support, tol):
"""Test that summation can be applied using all interfaces"""
if interface == "torch" and not torch_support:
pytest.skip("Skipped, no torch support")
if interface == "tf" and not tf_support:
pytest.skip("Skipped, no tf support")
qnode1, qnode2 = qnodes
qc = qml.QNodeCollection([qnode1, qnode2])
cost = qml.sum(qc)
params = [0.5643, -0.45]
res = cost(params)
expected = sum(qc[0](params) + qc[1](params))
if interface in ("tf", "torch"):
res = res.numpy()
expected = expected.numpy()
assert np.allclose(res, expected, atol=tol, rtol=0)
def __init__(self, n_qubits, q_depth, q_delta=0.1):
"""
This is the quantum generator as described in https://arxiv.org/pdf/2010.06201.pdf
"""
super().__init__()
self.q_params = nn.ParameterList([
nn.Parameter(q_delta * torch.randn(q_depth * n_qubits))
for i in range(8)
])
self.n_qubits = n_qubits
self.q_depth = q_depth
# Spread of the random parameters for the paramaterised quantum gates
self.q_delta = q_delta
device = qml.device('lightning.qubit', wires=self.n_qubits)
self.quantum_sim = QuantumSim(n_qubits, q_depth)
self.qnodes = qml.QNodeCollection([
qml.QNode(self.quantum_sim.circuit, device, interface="torch")
for i in range(8)
])
return qml.expval(A1 @ B2)
@qml.qnode(dev)
def measure_A2B1():
bell_pair()
return qml.expval(A2 @ B1)
@qml.qnode(dev)
def measure_A2B2():
bell_pair()
return qml.expval(A2 @ B2)
circuits = qml.QNodeCollection(
[measure_A1B1, measure_A1B2, measure_A2B1, measure_A2B2])
# now we measure each circuit and construct the CHSH inequality
expvals = circuits()
# The CHSH operator is A1 @ B1 + A1 @ B2 + A2 @ B1 - A2 @ B2
CHSH_expval = np.sum(expvals[:3]) - expvals[3]
print(CHSH_expval)
##############################################################################
# The output here is :math:`2\sqrt{2}`, which is the maximal value of the
# CHSH inequality. States which have a value
# :math:`\langle CHSH \rangle \geq 2` can safely be considered
# "quantum".
#
# .. note:: In this situation "quantum" means that there is
qml.CZ(wires=[0, 1])
qml.CZ(wires=[1, 2])
qml.CZ(wires=[1, 3])
for i in range(n_wires):
qml.Rot(*params[0, 1, i], wires=i)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1)), qml.expval(
qml.PauliZ(2))
##############################################################################
# We finally combine the two devices into a :class:`~.pennylane.QNodeCollection` that uses the
# PyTorch interface:
qnodes = qml.QNodeCollection([
qml.QNode(circuit0, dev0, interface="torch"),
qml.QNode(circuit1, dev1, interface="torch")
])
##############################################################################
# Postprocessing into a prediction
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
#
# The ``predict_point`` function below allows us to find the ensemble prediction, as well as keeping
# track of the individual predictions from each QPU.
#
# We include a ``parallel`` keyword argument for evaluating the :class:`~.pennylane.QNodeCollection`
# in a parallel asynchronous manner. This feature requires the ``dask`` library, which can be
# installed using ``pip install "dask[delayed]"``. When ``parallel=True``, we are able to make
# predictions faster because we do not need to wait for one QPU to output before running on the
# other.
def test_sequence(self):
"""Test that the QNodeCollection is a sequence type"""
qc = qml.QNodeCollection()
assert isinstance(qc, Sequence)
def test_empty_init(self):
"""Test that an empty QNode collection can be initialized"""
qc = qml.QNodeCollection()
assert qc.qnodes == []
assert len(qc) == 0
