def test_tf(self, tol):
"""Tests that the output of the parameter-shift CV transform
can be executed using TF"""
tf = pytest.importorskip("tensorflow")
dev = qml.device("default.gaussian", wires=1)
params = tf.Variable([0.543, -0.654], dtype=tf.float64)
with tf.GradientTape() as t:
with qml.tape.JacobianTape() as tape:
qml.Squeezing(params[0], 0, wires=0)
qml.Rotation(params[1], wires=0)
qml.var(qml.X(wires=[0]))
tape.trainable_params = {0, 2}
tapes, fn = param_shift_cv(tape, dev)
jac = fn(
qml.execute(
tapes, dev, param_shift_cv, gradient_kwargs={"dev": dev}, interface="tf"
)
)
res = jac[0, 1]
r, phi = 1.0 * params
expected = np.array(
[
2 * np.exp(2 * r) * np.sin(phi) ** 2 - 2 * np.exp(-2 * r) * np.cos(phi) ** 2,
2 * np.sinh(2 * r) * np.sin(2 * phi),
]
)
assert np.allclose(jac, expected, atol=tol, rtol=0)
grad = t.jacobian(res, params)
expected = np.array(
[4 * np.cosh(2 * r) * np.sin(2 * phi), 4 * np.cos(2 * phi) * np.sinh(2 * r)]
)
assert np.allclose(grad, expected, atol=tol, rtol=0)
def test_torch(self, tol):
"""Tests that the output of the parameter-shift CV transform
can be executed using Torch."""
torch = pytest.importorskip("torch")
from pennylane.interfaces.torch import TorchInterface
dev = qml.device("default.gaussian", wires=1)
params = torch.tensor([0.543, -0.654],
dtype=torch.float64,
requires_grad=True)
with TorchInterface.apply(qml.tape.CVParamShiftTape()) as tape:
qml.Squeezing(params[0], 0, wires=0)
qml.Rotation(params[1], wires=0)
qml.var(qml.X(wires=[0]))
tapes, fn = qml.gradients.param_shift_cv(tape, dev)
jac = fn([t.execute(dev) for t in tapes])
r, phi = params.detach().numpy()
expected = np.array([
2 * np.exp(2 * r) * np.sin(phi)**2 -
2 * np.exp(-2 * r) * np.cos(phi)**2,
2 * np.sinh(2 * r) * np.sin(2 * phi),
])
assert np.allclose(jac.detach().numpy(), expected, atol=tol, rtol=0)
cost = jac[0, 1]
cost.backward()
hess = params.grad
expected = np.array([
4 * np.cosh(2 * r) * np.sin(2 * phi),
4 * np.cos(2 * phi) * np.sinh(2 * r)
])
assert np.allclose(hess.detach().numpy(), expected, atol=0.1, rtol=0)
def test_tensor_number_displaced_squeezed(self, dev, disp_sq_circuit, pars,
tol):
"""Test the variance of the TensorN observable for a squeezed displaced
state"""
# Checking the circuit variance and the analytic expression
def squared_term(a, r, phi):
"""Analytic expression for <N^2>"""
magnitude_squared = np.abs(a)**2
squared_term = (
-magnitude_squared + magnitude_squared**2 +
2 * magnitude_squared * np.cosh(2 * r) -
np.exp(-1j * phi) * a**2 * np.cosh(r) * np.sinh(r) -
np.exp(1j * phi) * np.conj(a)**2 * np.cosh(r) * np.sinh(r) +
np.sinh(r)**4 + np.cosh(r) * np.sinh(r) * np.sinh(2 * r))
return squared_term
var = disp_sq_circuit(pars)
n0 = np.sinh(rs0)**2 + np.abs(alpha0)**2
n1 = np.sinh(rs1)**2 + np.abs(alpha1)**2
expected = (squared_term(alpha0, rs0, phis0) *
squared_term(alpha1, rs1, phis1) - n0**2 * n1**2)
assert np.allclose(var, expected, atol=tol, rtol=0)
def test_torch(self, tol):
"""Tests that the output of the parameter-shift CV transform
can be executed using Torch."""
torch = pytest.importorskip("torch")
dev = qml.device("default.gaussian", wires=1)
params = torch.tensor([0.543, -0.654], dtype=torch.float64, requires_grad=True)
with qml.tape.JacobianTape() as tape:
qml.Squeezing(params[0], 0, wires=0)
qml.Rotation(params[1], wires=0)
qml.var(qml.X(wires=[0]))
tape.trainable_params = {0, 2}
tapes, fn = qml.gradients.param_shift_cv(tape, dev)
jac = fn(
qml.execute(tapes, dev, param_shift_cv, gradient_kwargs={"dev": dev}, interface="torch")
)
r, phi = params.detach().numpy()
expected = np.array(
[
2 * np.exp(2 * r) * np.sin(phi) ** 2 - 2 * np.exp(-2 * r) * np.cos(phi) ** 2,
2 * np.sinh(2 * r) * np.sin(2 * phi),
]
)
assert np.allclose(jac.detach().numpy(), expected, atol=tol, rtol=0)
cost = jac[0, 1]
cost.backward()
hess = params.grad
expected = np.array(
[4 * np.cosh(2 * r) * np.sin(2 * phi), 4 * np.cos(2 * phi) * np.sinh(2 * r)]
)
assert np.allclose(hess.detach().numpy(), expected, atol=0.1, rtol=0)
def test_finite_diff_squeezed(self, tol):
"""Test that the jacobian of the probability for a squeezed states is
approximated well with finite differences"""
cutoff = 5
dev = qml.device("strawberryfields.fock", wires=1, cutoff_dim=cutoff)
@qml.qnode(dev)
def circuit(r, phi):
qml.Squeezing(r, phi, wires=0)
return qml.probs(wires=[0])
r = 0.4
phi = -0.12
n = np.arange(cutoff)
# construct tape
circuit.construct([r, phi], {})
# differentiate with respect to parameter a
circuit.qtape.trainable_params = {0}
tapes, fn = qml.gradients.finite_diff(circuit.qtape)
res_F = fn(dev.batch_execute(tapes)).flatten()
assert res_F.shape == (cutoff,)
expected_gradient = (
np.abs(np.tanh(r)) ** n
* (1 + 2 * n - np.cosh(2 * r))
* fac(n)
/ (2 ** (n + 1) * np.cosh(r) ** 2 * np.sinh(r) * fac(n / 2) ** 2)
)
expected_gradient[n % 2 != 0] = 0
assert np.allclose(res_F, expected_gradient, atol=tol, rtol=0)
# re-construct tape to reset trainable_params
circuit.construct([r, phi], {})
# differentiate with respect to parameter phi
circuit.qtape.trainable_params = {1}
tapes, fn = qml.gradients.finite_diff(circuit.qtape)
res_F = fn(dev.batch_execute(tapes)).flatten()
expected_gradient = 0
assert np.allclose(res_F, expected_gradient, atol=tol, rtol=0)
def test_squeezed_mean_photon_variance(self, tol):
"""Test gradient of the photon variance of a displaced thermal state"""
dev = qml.device("default.gaussian", wires=1)
r = 0.12
phi = 0.105
with qml.tape.JacobianTape() as tape:
qml.Squeezing(r, 0, wires=0)
qml.Rotation(phi, wires=0)
qml.var(qml.X(wires=[0]))
tape.trainable_params = {0, 2}
tapes, fn = param_shift_cv(tape, dev)
grad = fn(dev.batch_execute(tapes))
expected = np.array([
2 * np.exp(2 * r) * np.sin(phi)**2 -
2 * np.exp(-2 * r) * np.cos(phi)**2,
2 * np.sinh(2 * r) * np.sin(2 * phi),
])
assert np.allclose(grad, expected, atol=tol, rtol=0)
def pd_sr(rs0, phis0, rd0, phid0, rs1, phis1, rd1, phid1):
"""Analytic expression for the partial derivative with respect to
the r argument of the first squeezing operation (rs0)"""
return (
(0.25 + rd0**2 * (-0.25 - 2 * rd1**2 + 2 * rd1**4) +
(-(rd1**2) + rd0**2 * (-1 + 6 * rd1**2)) * np.cosh(2 * rs1) +
(-0.25 + 1.25 * rd0**2) * np.cosh(4 * rs1)) * np.sinh(2 * rs0)
+ (-(rd1**2) + rd1**4 +
(-0.5 + 2.5 * rd1**2) * np.cosh(2 * rs1) +
0.5 * np.cosh(4 * rs1)) * np.sinh(4 * rs0) +
rd1**2 * np.cos(2 * phid1 - phis1) *
((1 - 4 * rd0**2) * np.sinh(2 * rs0) - 1.5 * np.sinh(4 * rs0))
* np.sinh(2 * rs1) +
rd0**2 * np.cos(2 * phid0 - phis0) * np.cosh(2 * rs0) *
(-0.25 + 2 * rd1**2 - 2 * rd1**4 +
(1 - 4 * rd1**2) * np.cosh(2 * rs1) - 0.75 * np.cosh(4 * rs1)
+ 2 * rd1**2 * np.cos(2 * phid1 - phis1) * np.sinh(2 * rs1)))
def test_autograd_gradient(self, tol):
"""Tests that the output of the parameter-shift CV transform
can be differentiated using autograd, yielding second derivatives."""
dev = qml.device("default.gaussian", wires=1)
r = 0.12
phi = 0.105
def cost_fn(x):
with qml.tape.JacobianTape() as tape:
qml.Squeezing(x[0], 0, wires=0)
qml.Rotation(x[1], wires=0)
qml.var(qml.X(wires=[0]))
tapes, fn = param_shift_cv(tape, dev)
jac = fn(qml.execute(tapes, dev, param_shift_cv, gradient_kwargs={"dev": dev}))
return jac[0, 2]
params = np.array([r, phi], requires_grad=True)
grad = qml.jacobian(cost_fn)(params)
expected = np.array(
[4 * np.cosh(2 * r) * np.sin(2 * phi), 4 * np.cos(2 * phi) * np.sinh(2 * r)]
)
assert np.allclose(grad, expected, atol=tol, rtol=0)
