def test_param_unused(self, operable_mock_device_2_wires):
"""Test that the gradient is 0 of an unused parameter"""
def circuit(x, y):
qml.RX(x, wires=[0])
return qml.expval(qml.PauliZ(0))
q = JacobianQNode(circuit, operable_mock_device_2_wires)
q._construct([1.0, 1.0], {})
assert q.par_to_grad_method == {0: "F", 1: "0"}
def test_no_following_observable(self, operable_mock_device_2_wires):
"""Test that the gradient is 0 if no observables succeed"""
def circuit(x):
qml.RX(x, wires=[1])
return qml.expval(qml.PauliZ(0))
q = JacobianQNode(circuit, operable_mock_device_2_wires)
q._construct([1.0], {})
assert q.par_to_grad_method == {0: "0"}
def test_all_finite_difference(self, operable_mock_device_2_wires):
"""Finite difference is the best method in almost all cases"""
def circuit(x, y, z):
qml.Rot(x, y, z, wires=[0])
return qml.expval(qml.PauliZ(0))
q = JacobianQNode(circuit, operable_mock_device_2_wires)
q._construct([1.0, 1.0, 1.0], {})
assert q.par_to_grad_method == {0: "F", 1: "F", 2: "F"}
def test_not_differentiable(self, operable_mock_device_2_wires):
"""Test that an operation with grad_method=None is marked as
non-differentiable"""
def circuit(x):
qml.BasisState(x, wires=[1])
return qml.expval(qml.PauliZ(0))
q = JacobianQNode(circuit, operable_mock_device_2_wires)
q._construct([np.array([1.0])], {})
assert q.par_to_grad_method == {0: None}
def test_unknown_gradient_method(self, operable_mock_device_2_wires):
""" The gradient method is unknown."""
def circuit(x):
qml.Rot(0.3, x, -0.2, wires=[0])
return qml.expval(qml.PauliZ(0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="Unknown gradient method"):
node.jacobian(0.5, method="unknown")
def test_wrong_order_in_finite_difference(self, operable_mock_device_2_wires):
"""Finite difference are attempted with wrong order."""
def circuit(x):
qml.Rot(0.3, x, -0.2, wires=[0])
return qml.expval(qml.PauliZ(0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="Order must be 1 or 2"):
node.jacobian(0.5, method="F", options={'order': 3})
def test_indices_not_unique(self, operable_mock_device_2_wires):
"""The Jacobian is requested for non-unique indices."""
def circuit(x):
qml.Rot(0.3, x, -0.2, wires=[0])
return qml.expval(qml.PauliZ(0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="Parameter indices must be unique."):
node.jacobian(0.5, wrt=[0, 0])
def test_operator_not_supporting_pd_analytic(self, operable_mock_device_2_wires):
"""Differentiating wrt. a parameter that appears
as an argument to an operation that does not support parameter-shift derivatives."""
def circuit(x):
qml.RX(x, wires=[0])
return qml.expval(qml.Hermitian(np.diag([x, 0]), 0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="analytic gradient method cannot be used with"):
node.jacobian(0.5, method="A")
def test_gradient_of_sample(self, operable_mock_device_2_wires):
"""Differentiation of a sampled output."""
def circuit(x):
qml.RX(x, wires=[0])
return qml.sample(qml.PauliZ(0)), qml.sample(qml.PauliX(1))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(QuantumFunctionError,
match="Circuits that include sampling can not be differentiated."):
node.jacobian(1.0)
def test_nondifferentiable_operator(self, operable_mock_device_2_wires):
"""Differentiating wrt. a parameter
that appears as an argument to a nondifferentiable operator."""
def circuit(x):
qml.BasisState(np.array([x, 0]), wires=[0, 1]) # not differentiable
qml.RX(x, wires=[0])
return qml.expval(qml.PauliZ(0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="Cannot differentiate with respect to the parameters"):
node.jacobian(0.5)
def test_interface_str(self, qubit_device_2_wires):
"""Test that the interface string is correctly identified
as None"""
def circuit(x, y, z):
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))
circuit = JacobianQNode(circuit, qubit_device_2_wires)
assert circuit.interface == None
def test_finite_difference_options(qubit_device_1_wire, order, h, monkeypatch):
"""Test that the _pd_finite_diff method for calculating the finite difference gradient
correctly receives the options set when the JacobianQNode was instantiated."""
def circuit(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
q = JacobianQNode(circuit, qubit_device_1_wire, order=order, h=h)
_pd_finite_diff = mock.MagicMock(return_value=0)
with monkeypatch.context() as m:
m.setattr("pennylane.qnodes.jacobian.JacobianQNode._pd_finite_diff", _pd_finite_diff)
q.jacobian(0.2)
call_kwargs = _pd_finite_diff.call_args[1]
assert call_kwargs["order"] == order
assert call_kwargs["h"] == h
def test_bogus_gradient_method_set(self, operable_mock_device_2_wires):
"""The gradient method set is bogus."""
def circuit(x):
qml.RX(x, wires=[0])
return qml.expval(qml.PauliZ(0))
# in mutable mode, the grad method would be
# recomputed and overwritten from the
# bogus value 'J'. Caching stops this from happening.
node = JacobianQNode(circuit, operable_mock_device_2_wires, mutable=False)
node.evaluate([0.0], {})
node.par_to_grad_method[0] = "J"
with pytest.raises(ValueError, match="Unknown gradient method"):
node.jacobian(0.5)
def test_indices_nonexistant(self, operable_mock_device_2_wires):
""" The Jacobian is requested for non-existant parameters."""
def circuit(x):
qml.Rot(0.3, x, -0.2, wires=[0])
return qml.expval(qml.PauliZ(0))
node = JacobianQNode(circuit, operable_mock_device_2_wires)
with pytest.raises(ValueError, match="Tried to compute the gradient with respect to"):
node.jacobian(0.5, wrt=[0, 6])
with pytest.raises(ValueError, match="Tried to compute the gradient with respect to"):
node.jacobian(0.5, wrt=[1, -1])
