def test_tensorn_one_mode_is_mean_photon(self, tol):
"""Test variance of TensorN for a single mode, which resorts to
calculations for the NumberOperator"""
dev = qml.device("strawberryfields.gaussian", wires=1)
op = qml.TensorN(wires=[0])
# Check that instantiating TensorN on one mode returns the
# NumberOperator
assert isinstance(op, qml.NumberOperator)
@qml.qnode(dev)
def circuit(n, a):
qml.ThermalState(n, wires=0)
qml.Displacement(a, 0, wires=0)
return qml.var(op)
n = 0.12
a = 0.765
var = circuit(n, a)
expected = n**2 + n + np.abs(a)**2 * (1 + 2 * n)
assert np.allclose(var, expected, atol=tol, rtol=0)
# circuit jacobians
gradF = circuit.jacobian([n, a], method="F")
expected = np.array([2 * a**2 + 2 * n + 1, 2 * a * (2 * n + 1)])
assert np.allclose(gradF, expected, atol=tol, rtol=0)
def test_tensorn_one_mode_is_mean_photon(self, tol):
"""Test variance of TensorN for a single mode, which resorts to
calculations for the NumberOperator"""
dev = qml.device("strawberryfields.fock", wires=1, cutoff_dim=15)
op = qml.TensorN(wires=[0])
# Check that instantiating TensorN on one mode returns the
# NumberOperator
assert isinstance(op, qml.NumberOperator)
@qml.qnode(dev)
def circuit(n, a):
qml.ThermalState(n, wires=0)
qml.Displacement(a, 0, wires=0)
return qml.var(op)
n = np.array(0.12, requires_grad=True)
a = np.array(0.105, requires_grad=True)
var = circuit(n, a)
expected = n**2 + n + np.abs(a) ** 2 * (1 + 2 * n)
assert np.allclose(var, expected, atol=tol, rtol=0)
# circuit jacobians
tapes, fn = qml.gradients.finite_diff(circuit.qtape)
gradF = fn(dev.batch_execute(tapes)).flatten()
expected = np.array([2 * a**2 + 2 * n + 1, 2 * a * (2 * n + 1)])
assert np.allclose(gradF, expected, atol=tol, rtol=0)
def test_gradient_displaced_thermal_var_photon(self, tol):
"""Test gradient of the photon variance of a displaced thermal state"""
dev = qml.device("strawberryfields.tf", wires=1, cutoff_dim=15)
op = qml.TensorN(wires=[0])
# Check that instantiating TensorN on one mode returns the
# NumberOperator
assert isinstance(op, qml.NumberOperator)
@qml.qnode(dev, interface="tf", diff_method="backprop")
def circuit(n, a):
qml.ThermalState(n, wires=0)
qml.Displacement(a, 0, wires=0)
return qml.var(op)
n = tf.Variable(0.12)
a = tf.Variable(0.105)
with tf.GradientTape(persistent=True) as tape:
var = circuit(n, a)
# circuit jacobians
grad = tape.gradient(var, [n, a])
expected = np.array([2 * a**2 + 2 * n + 1, 2 * a * (2 * n + 1)])
assert np.allclose(grad, expected, atol=tol, rtol=0)
def test_tensor_n_single_mode_wires_implicit(self):
"""Checks that instantiating TensorN when passing a single mode as a
positional argument returns a NumberOperator."""
cv_obs = qml.TensorN(1)
assert isinstance(cv_obs, qml.NumberOperator)
assert cv_obs.wires == Wires([1])
assert cv_obs.ev_order == 2
def test_tensor_n_single_mode_wires_explicit(self):
"""Checks that instantiating a TensorN when passing a single mode as a
keyword argument returns a NumberOperator."""
cv_obs = qml.TensorN(wires=[0])
assert isinstance(cv_obs, qml.NumberOperator)
assert cv_obs.wires == Wires([0])
assert cv_obs.ev_order == 2
def test_tensor_n_multiple_modes(self):
"""Checks that the TensorN operator was constructed correctly when
multiple modes were specified."""
cv_obs = qml.TensorN(wires=[0, 1])
assert isinstance(cv_obs, qml.TensorN)
assert cv_obs.wires == Wires([0, 1])
assert cv_obs.ev_order is None
def test_displaced_thermal_mean_photon_variance(self, tol):
"""Test gradient of the photon variance of a displaced thermal state"""
dev = qml.device("default.gaussian", wires=1)
n = 0.12
a = 0.105
with CVParamShiftTape() as tape:
qml.ThermalState(n, wires=0)
qml.Displacement(a, 0, wires=0)
var(qml.TensorN(wires=[0]))
tape.trainable_params = {0, 1}
grad = tape.jacobian(dev)
expected = np.array([2 * a ** 2 + 2 * n + 1, 2 * a * (2 * n + 1)])
assert np.allclose(grad, expected, atol=tol, rtol=0)
def test_displaced_thermal_mean_photon_variance(self, tol):
"""Test gradient of the photon variance of a displaced thermal state"""
dev = qml.device("default.gaussian", wires=1)
n = 0.12
a = 0.105
with qml.tape.JacobianTape() as tape:
qml.ThermalState(n, wires=0)
qml.Displacement(a, 0, wires=0)
qml.var(qml.TensorN(wires=[0]))
tape.trainable_params = {0, 1}
tapes, fn = param_shift_cv(tape, dev)
grad = fn(dev.batch_execute(tapes))
expected = np.array([2 * a**2 + 2 * n + 1, 2 * a * (2 * n + 1)])
assert np.allclose(grad, expected, atol=tol, rtol=0)
def circuit(theta, phi):
qml.Beamsplitter(theta, phi, wires=[wires[0], wires[1]])
qml.Beamsplitter(theta, phi, wires=[wires[4], wires[5]])
return qml.expval(qml.TensorN(wires=wires))
def circuit(pars):
qml.Squeezing(pars[0], pars[1], wires=0)
qml.Displacement(pars[2], pars[3], wires=0)
qml.Squeezing(pars[4], pars[5], wires=1)
qml.Displacement(pars[6], pars[7], wires=1)
return qml.var(qml.TensorN(wires=[0, 1]))
def circuit(a, phi):
qml.Displacement(a, phi, wires=0)
qml.Displacement(a, phi, wires=1)
return qml.var(qml.TensorN(wires=[0, 1]))
