def test_array_parameters_autograd(self, tol, qubit_device_2_wires):
"""Test that gradients of array parameters give
same results as positional arguments."""
par = [0.5, 0.54, 0.3]
def ansatz(x, y, z):
qml.QubitStateVector(np.array([1, 0, 1, 1]) / np.sqrt(3), wires=[0, 1])
qml.Rot(x, y, z, wires=0)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0))
def circuit1(x, y, z):
return ansatz(x, y, z)
def circuit2(x, array):
return ansatz(x, array[0], array[1])
def circuit3(array):
return ansatz(*array)
circuit1 = to_autograd(QubitQNode(circuit1, qubit_device_2_wires))
grad1 = autograd.grad(circuit1, argnum=[0, 1, 2])
# three positional parameters
jac = circuit1.jacobian(par)
ag = grad1(*par)
ag = np.array([ag])
assert jac == pytest.approx(ag, abs=tol)
circuit2 = to_autograd(QubitQNode(circuit2, qubit_device_2_wires))
grad2 = autograd.grad(circuit2, argnum=[0, 1])
# one scalar, one array
temp = [par[0], np.array(par[1:])]
jac = circuit2.jacobian(temp)
ag = grad2(*temp)
ag = np.r_[ag][np.newaxis, :]
assert jac == pytest.approx(ag, abs=tol)
circuit3 = to_autograd(QubitQNode(circuit3, qubit_device_2_wires))
grad3 = autograd.grad(circuit3, argnum=0)
# one array
temp = [np.array(par)]
jac = circuit3.jacobian(temp)
ag = grad3(*temp)[np.newaxis, :]
assert jac == pytest.approx(ag, abs=tol)
def test_hybrid_gradients_autograd_numpy(self, qubit_device_2_wires, tol):
"Test the gradient of a hybrid computation requiring autograd.numpy functions."
def circuit(x, y):
"Quantum node."
qml.RX(x, wires=[0])
qml.CNOT(wires=[0, 1])
qml.RY(y, wires=[0])
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliZ(1))
quantum = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
def classical(p):
"Classical node, requires autograd.numpy functions."
return anp.exp(anp.sum(quantum(p[0], anp.log(p[1]))))
def d_classical(a, b, method):
"Gradient of classical computed symbolically, can use normal numpy functions."
val = classical((a, b))
J = quantum.jacobian((a, np.log(b)), method=method)
return val * np.array([J[0, 0] + J[1, 0], (J[0, 1] + J[1, 1]) / b])
param = np.array([-0.1259, 1.53])
y0 = classical(param)
grad_classical = autograd.jacobian(classical)
grad_auto = grad_classical(param)
grad_fd1 = d_classical(*param, 'F')
grad_angle = d_classical(*param, 'A')
# gradients computed with different methods must agree
assert grad_fd1 == pytest.approx(grad_angle, abs=tol)
assert grad_fd1 == pytest.approx(grad_auto, abs=tol)
assert grad_angle == pytest.approx(grad_auto, abs=tol)
def test_qfunc_gradients(self, qubit_device_2_wires, tol):
"Tests that the various ways of computing the gradient of a qfunc all agree."
def circuit(x, y, z):
qml.RX(x, wires=[0])
qml.CNOT(wires=[0, 1])
qml.RY(-1.6, wires=[0])
qml.RY(y, wires=[1])
qml.CNOT(wires=[1, 0])
qml.RX(z, wires=[0])
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0))
qnode = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
params = np.array([0.1, -1.6, np.pi / 5])
# manual gradients
grad_fd1 = qnode.jacobian(params, method='F', options={'order': 1})
grad_fd2 = qnode.jacobian(params, method='F', options={'order': 2})
grad_angle = qnode.jacobian(params, method='A')
# automatic gradient
grad_fn = autograd.grad(qnode, argnum=[0, 1, 2])
grad_auto = np.array([grad_fn(*params)])
# gradients computed with different methods must agree
assert grad_fd1 == pytest.approx(grad_fd2, abs=tol)
assert grad_fd1 == pytest.approx(grad_angle, abs=tol)
assert grad_fd1 == pytest.approx(grad_auto, abs=tol)
def test_qnode_array_parameters_2_vector_return(self, qubit_device_2_wires, tol):
"""Test that QNode can take arrays as input arguments, and that they interact properly with Autograd.
Test case for a circuit that returns a 2-vector."""
def circuit(dummy1, array, dummy2):
qml.RY(0.5 * array[0, 1], wires=0)
qml.RY(-0.5 * array[1, 1], wires=0)
qml.RY(array[1, 0], wires=1)
return qml.expval(qml.PauliX(0)), qml.expval(qml.PauliX(1))
node = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
args = (0.46, np.array([[2.0, 3.0, 0.3], [7.0, 4.0, 2.1]]), -0.13)
grad_target = (
np.array(1.0),
np.array([[0.5, 0.43879, 0], [0, -0.43879, 0]]),
np.array(-0.4),
)
cost_target = 1.03257
def cost(x, array, y):
c = node(0.111, array, 4.5)[0]
return c + 0.5 * array[0, 0] + x - 0.4 * y
cost_grad = autograd.grad(cost, argnum=[0, 1, 2])
computed_grad = cost_grad(*args)
assert cost(*args) == pytest.approx(cost_target, abs=tol)
assert computed_grad[0] == pytest.approx(grad_target[0], abs=tol)
assert computed_grad[1] == pytest.approx(grad_target[1], abs=tol)
assert computed_grad[2] == pytest.approx(grad_target[2], abs=tol)
def test_multiple_expectation_jacobian_positional(self, tol, qubit_device_2_wires):
"""Tests that qnodes using positional arguments return
correct gradients for multiple expectation values."""
par = [0.5, 0.54, 0.3]
def circuit(x, y, z):
qml.QubitStateVector(np.array([1, 0, 1, 1]) / np.sqrt(3), wires=[0, 1])
qml.Rot(x, y, z, wires=0)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))
circuit = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
# compare our manual Jacobian computation to theoretical result
expected_jac = self.expected_jacobian(*par)
res = circuit.jacobian(par)
assert expected_jac == pytest.approx(res, abs=tol)
# compare our manual Jacobian computation to autograd
# not sure if this is the intended usage of jacobian
jac0 = autograd.jacobian(circuit, 0)
jac1 = autograd.jacobian(circuit, 1)
jac2 = autograd.jacobian(circuit, 2)
res = np.stack([jac0(*par), jac1(*par), jac2(*par)]).T
assert expected_jac == pytest.approx(res, abs=tol)
#compare with what we get if argnum is a list
jac = autograd.jacobian(circuit, argnum=[0, 1, 2])
def test_interface_str(self, qubit_device_2_wires):
"""Test that the interface string is correctly identified
as numpy"""
def circuit(x, y, z):
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))
circuit = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
assert circuit.interface == "autograd"
def test_number_state_gradient(self, r, tol):
"""Tests that the automatic gradient of a squeezed state with number state expectation is correct."""
def circuit(y):
qml.Squeezing(y, 0., wires=[0])
return qml.expval(qml.FockStateProjector(np.array([2, 0]), wires=[0, 1]))
dev = qml.device('default.gaussian', wires=2)
circuit = to_autograd(CVQNode(circuit, dev))
grad_fn = autograd.grad(circuit)
# (d/dr) |<2|S(r)>|^2 = 0.5 tanh(r)^3 (2 csch(r)^2 - 1) sech(r)
autograd_val = grad_fn(r)
manualgrad_val = 0.5*np.tanh(r)**3 * (2/(np.sinh(r)**2)-1) / np.cosh(r)
assert autograd_val == pytest.approx(manualgrad_val, abs=tol)
def test_hybrid_gradients(self, qubit_device_2_wires, tol):
"Tests that the various ways of computing the gradient of a hybrid computation all agree."
# input data is the first parameter
def classifier_circuit(in_data, x):
qml.RX(in_data, wires=[0])
qml.CNOT(wires=[0, 1])
qml.RY(-1.6, wires=[0])
qml.RY(in_data, wires=[1])
qml.CNOT(wires=[1, 0])
qml.RX(x, wires=[0])
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0))
classifier = to_autograd(QubitQNode(classifier_circuit, qubit_device_2_wires))
param = -0.1259
in_data = np.array([-0.1, -0.88, np.exp(0.5)])
out_data = np.array([1.5, np.pi / 3, 0.0])
def error(p):
"Total square error of classifier predictions."
ret = 0
for d_in, d_out in zip(in_data, out_data):
square_diff = (classifier(d_in, p) - d_out) ** 2
ret = ret + square_diff
return ret
def d_error(p, grad_method):
"Gradient of error, computed manually."
ret = 0
for d_in, d_out in zip(in_data, out_data):
args = (d_in, p)
diff = (classifier(*args) - d_out)
ret = ret + 2 * diff * classifier.jacobian(args, wrt=[1], method=grad_method)
return ret
y0 = error(param)
grad = autograd.grad(error)
grad_auto = grad(param)
grad_fd1 = d_error(param, 'F')
grad_angle = d_error(param, 'A')
# gradients computed with different methods must agree
assert grad_fd1 == pytest.approx(grad_angle, abs=tol)
assert grad_fd1 == pytest.approx(grad_auto, abs=tol)
assert grad_angle == pytest.approx(grad_auto, abs=tol)
def test_displacement_gradient(self, mag, theta, tol):
"""Tests that the automatic gradient of a phase space displacement is correct."""
def circuit(r, phi):
qml.Displacement(r, phi, wires=[0])
return qml.expval(qml.X(0))
dev = qml.device('default.gaussian', wires=1)
circuit = to_autograd(CVQNode(circuit, dev))
grad_fn = autograd.grad(circuit)
#alpha = mag * np.exp(1j * theta)
autograd_val = grad_fn(mag, theta)
# qfunc evalutes to hbar * Re(alpha)
manualgrad_val = hbar * np.cos(theta)
assert autograd_val == pytest.approx(manualgrad_val, abs=tol)
def test_beamsplitter_gradient(self, theta, tol):
"""Tests that the automatic gradient of a beamsplitter is correct."""
def circuit(y):
qml.Displacement(alpha, 0., wires=[0])
qml.Beamsplitter(y, 0, wires=[0, 1])
return qml.expval(qml.X(0))
dev = qml.device('default.gaussian', wires=2)
circuit = to_autograd(CVQNode(circuit, dev))
grad_fn = autograd.grad(circuit)
autograd_val = grad_fn(theta)
# qfunc evalutes to hbar * alpha * cos(theta)
manualgrad_val = - hbar * alpha * np.sin(theta)
assert autograd_val == pytest.approx(manualgrad_val, abs=tol)
def test_rotation_gradient(self, theta, tol):
"""Tests that the automatic gradient of a phase space rotation is correct."""
def circuit(y):
qml.Displacement(alpha, 0., wires=[0])
qml.Rotation(y, wires=[0])
return qml.expval(qml.X(0))
dev = qml.device('default.gaussian', wires=1)
circuit = to_autograd(QubitQNode(circuit, dev))
grad_fn = autograd.grad(circuit)
autograd_val = grad_fn(theta)
# qfunc evalutes to hbar * alpha * cos(theta)
manualgrad_val = - hbar * alpha * np.sin(theta)
assert autograd_val == pytest.approx(manualgrad_val, abs=tol)
def test_squeeze_gradient(self, r, tol):
"""Tests that the automatic gradient of a phase space squeezing is correct."""
def circuit(y):
qml.Displacement(alpha, 0., wires=[0])
qml.Squeezing(y, 0., wires=[0])
return qml.expval(qml.X(0))
dev = qml.device('default.gaussian', wires=1)
circuit = to_autograd(CVQNode(circuit, dev))
grad_fn = autograd.grad(circuit)
autograd_val = grad_fn(r)
# qfunc evaluates to -exp(-r) * hbar * Re(alpha)
manualgrad_val = -np.exp(-r) * hbar * alpha
assert autograd_val == pytest.approx(manualgrad_val, abs=tol)
def test_multiple_expectation_jacobian_array(self, tol, qubit_device_2_wires):
"""Tests that qnodes using an array argument return correct gradients
for multiple expectation values."""
par = np.array([0.5, 0.54, 0.3])
def circuit(weights):
qml.QubitStateVector(np.array([1, 0, 1, 1]) / np.sqrt(3), wires=[0, 1])
qml.Rot(weights[0], weights[1], weights[2], wires=0)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliZ(0)), qml.expval(qml.PauliY(1))
circuit = to_autograd(QubitQNode(circuit, qubit_device_2_wires))
expected_jac = self.expected_jacobian(*par)
res = circuit.jacobian([par])
assert expected_jac == pytest.approx(res, abs=tol)
jac = autograd.jacobian(circuit, 0)
res = jac(par)
assert expected_jac == pytest.approx(res, abs=tol)