def test_circuit_with_decomposition(self, tol):
"""Tests a PassthruQNode in which the circuit contains an operation
that needs to be decomposed."""
theta = 0.543
phi = -0.234
lam = 0.654
p = [theta, phi, lam]
dev = qml.device("default.tensor.tf", wires=1)
def circuit(weights):
qml.QubitStateVector(1j * np.array([1, -1]) / np.sqrt(2),
wires=[0])
qml.U3(weights[0], weights[1], weights[2],
wires=[0]) # <--- decomposition is required
return qml.expval(qml.PauliX(0))
node = PassthruQNode(circuit, dev, interface="tf")
params = tf.Variable(p, dtype=tf.float64)
with tf.GradientTape() as tape:
tape.watch(params)
res = node(params)
expected = np.sin(lam) * np.sin(phi) - np.cos(theta) * np.cos(
lam) * np.cos(phi)
assert np.allclose(res, expected, atol=tol, rtol=0)
def test_evaluate(self, tensornet_tf_device, tol):
"""Tests the evaluation of a PassthruQNode."""
def circuit(x):
qml.RX(x[0], wires=0)
qml.RY(x[1], wires=1)
qml.CNOT(wires=[0, 1])
qml.RX(x[2], wires=1)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
x = np.array([-1.4, 0.2, 2.1])
x_tf = tf.Variable(x, dtype=self.DTYPE)
node = PassthruQNode(circuit, tensornet_tf_device)
res = node(x_tf)
# compare to a reference node
#ref_device = qml.device('default.qubit', wires=2)
#ref_node = BaseQNode(circuit, ref_device)
#ref_res = ref_node(x)
# analytic result
ref_res = np.cos(x[0]) * np.array([1.0, np.cos(x[1]) * np.cos(x[2])])
assert isinstance(res, tf.Tensor)
assert res.shape == (2, )
assert res.dtype == self.DTYPE
assert res.numpy() == pytest.approx(ref_res, abs=tol)
def mock_qnode(mock_device):
"""Simple PassthruQNode with default properties."""
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
node = PassthruQNode(circuit, mock_device)
return node
def test_immutable_error(self, mock_device):
"""Test that an error is raised if the mutable=False option is passed
upon instantiation"""
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
with pytest.raises(ValueError, match=r"PassthruQNode does not support immutable mode"):
node = PassthruQNode(circuit, mock_device, mutable=False)
def test_arraylike_keywordargs(self, tensornet_tf_device, tol):
"""Tests that qnodes use array-like TF objects as keyword-only arguments."""
def circuit(*, x=None):
qml.RX(x[0], wires=[0])
qml.RX(2 * x[1], wires=[1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
node = PassthruQNode(circuit, tensornet_tf_device)
x = tf.Variable([1.1, 1.4], dtype=self.DTYPE)
res = node(x=x)
assert isinstance(res, tf.Tensor)
assert res.shape == (2, )
assert res.dtype == self.DTYPE
def test_jacobian(self, tensornet_tf_device, vectorize_jacobian, tol):
"""Tests the computing of the Jacobian of a PassthruQNode using TensorFlow."""
def circuit(phi, theta):
qml.RX(phi[0], wires=0)
qml.RY(phi[1], wires=1)
qml.CNOT(wires=[0, 1])
qml.RX(theta, wires=1)
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
ph = np.array([0.7, -1.2])
th = 1.7
phi = tf.Variable(ph, dtype=self.DTYPE)
theta = tf.Variable(th, dtype=self.DTYPE)
node = PassthruQNode(circuit, tensornet_tf_device)
# In TF 2, tf.GradientTape.jacobian comes with a vectorization option.
with tf.GradientTape(persistent=not vectorize_jacobian) as tape:
tape.watch([phi, theta])
res = node(phi, theta)
phi_grad, theta_grad = tape.jacobian(
res, [phi, theta],
unconnected_gradients=tf.UnconnectedGradients.ZERO,
experimental_use_pfor=vectorize_jacobian)
# compare to a reference Jacobian computed using finite differences
#ref_device = qml.device('default.qubit', wires=2)
#ref_node = JacobianQNode(circuit, ref_device)
#ref_jac = ref_node.jacobian([ph, th])
# analytic result
ref_jac = np.array([[-np.sin(ph[0]), 0., 0.],
[
-np.sin(ph[0]) * np.cos(ph[1]) * np.cos(th),
np.cos(ph[0]) * -np.sin(ph[1]) * np.cos(th),
np.cos(ph[0]) * np.cos(ph[1]) * -np.sin(th)
]])
assert isinstance(phi_grad, tf.Tensor)
assert phi_grad.shape == (2, 2)
assert phi_grad.dtype == self.DTYPE
assert phi_grad.numpy() == pytest.approx(ref_jac[:, 0:2], abs=tol)
assert isinstance(theta_grad, tf.Tensor)
assert theta_grad.shape == (2, )
assert theta_grad.dtype == self.DTYPE
assert theta_grad.numpy() == pytest.approx(ref_jac[:, 2], abs=tol)
def test_tensor_operations(self, tensornet_tf_device, tol):
"""Tests the evaluation of a PassthruQNode involving algebraic operations between tensor parameters,
and TF functions acting on them."""
def circuit(phi, theta):
x = phi * theta
qml.RX(x[0], wires=0)
qml.RY(tf.cos(x[1]), wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.Hadamard(1))
node = PassthruQNode(circuit, tensornet_tf_device)
phi = tf.Variable(np.array([0.7, -1.2]), dtype=self.DTYPE)
theta = tf.Variable(1.7, dtype=self.DTYPE)
res = node(phi, theta)
assert isinstance(res, tf.Tensor)
assert res.shape == (2, )
assert res.dtype == self.DTYPE