def test_incorrect_heisenberg_size(self, monkeypatch):
"""The number of dimensions of a CV observable Heisenberg representation does
not match the ev_order attribute."""
monkeypatch.setattr(qml.P, "ev_order", 2)
with pytest.raises(ValueError, match="Mismatch between the polynomial order"):
_transform_observable(qml.P(0), np.identity(3), device_wires=[0])
def test_vacuum_state(self):
"""Test the vacuum state is correct."""
self.logTestName()
wires = 3
means, cov = vacuum_state(wires, hbar=hbar)
self.assertAllAlmostEqual(means, np.zeros([2*wires]), delta=self.tol)
self.assertAllAlmostEqual(cov, np.identity(2*wires)*hbar/2, delta=self.tol)
def test_controlled_arbitrary_rotation(self):
"""Test controlled arbitrary rotation is correct"""
self.logTestName()
# test identity for phi,theta,omega=0
self.assertAllAlmostEqual(CRot3(0,0,0), np.identity(4), delta=self.tol)
# test identity for phi,theta,omega=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1], [0, 0, 1, 0]])
self.assertAllAlmostEqual(CRot3(np.pi,np.pi,np.pi), expected, delta=self.tol)
def arbitrary_Crotation(x, y, z):
"""controlled arbitrary single qubit rotation"""
c = np.cos(y / 2)
s = np.sin(y / 2)
return np.array(
[
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],
[0, 0, np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c]
]
)
a, b, c = 0.432, -0.152, 0.9234
self.assertAllAlmostEqual(
CRot3(a, b, c), arbitrary_Crotation(a, b, c), delta=self.tol
)
def test_coherent_state(self):
"""Test the coherent state is correct."""
self.logTestName()
a = 0.432-0.123j
means, cov = coherent_state(a, hbar=hbar)
self.assertAllAlmostEqual(means, np.array([a.real, a.imag])*np.sqrt(2*hbar), delta=self.tol)
self.assertAllAlmostEqual(cov, np.identity(2)*hbar/2, delta=self.tol)
def test_higher_order_observable(self, monkeypatch):
"""An exception should be raised if the observable is higher than 2nd order."""
monkeypatch.setattr(qml.P, "ev_order", 3)
with pytest.raises(NotImplementedError,
match="order > 2 not implemented"):
_transform_observable(qml.P(0), np.identity(3), device_wires=[0])
def test_controlled_arbitrary_rotation(self, tol):
"""Test controlled arbitrary rotation is correct"""
# test identity for phi,theta,omega=0
assert np.allclose(CRot3(0, 0, 0), np.identity(4), atol=tol, rtol=0)
# test identity for phi,theta,omega=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1], [0, 0, 1, 0]])
assert np.allclose(CRot3(np.pi, np.pi, np.pi), expected, atol=tol, rtol=0)
def arbitrary_Crotation(x, y, z):
"""controlled arbitrary single qubit rotation"""
c = np.cos(y / 2)
s = np.sin(y / 2)
return np.array(
[
[1, 0, 0, 0],
[0, 1, 0, 0],
[0, 0, np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],
[0, 0, np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c]
]
)
a, b, c = 0.432, -0.152, 0.9234
assert np.allclose(CRot3(a, b, c), arbitrary_Crotation(a, b, c), atol=tol, rtol=0)
def step_and_cost(self,
qnode,
*args,
grad_fn=None,
recompute_tensor=True,
metric_tensor_fn=None,
**kwargs):
"""Update the parameter array :math:`x` with one step of the optimizer and return the
corresponding objective function value prior to the step.
Args:
qnode (QNode): the QNode for optimization
*args : variable length argument list for qnode
grad_fn (function): optional gradient function of the
qnode with respect to the variables ``*args``.
If ``None``, the gradient function is computed automatically.
Must return a ``tuple[array]`` with the same number of elements as ``*args``.
Each array of the tuple should have the same shape as the corresponding argument.
recompute_tensor (bool): Whether or not the metric tensor should
be recomputed. If not, the metric tensor from the previous
optimization step is used.
metric_tensor_fn (function): Optional metric tensor function
with respect to the variables ``args``.
If ``None``, the metric tensor function is computed automatically.
**kwargs : variable length of keyword arguments for the qnode
Returns:
tuple: the new variable values :math:`x^{(t+1)}` and the objective function output
prior to the step
"""
# pylint: disable=arguments-differ
if not isinstance(
qnode,
(qml.QNode, qml.ExpvalCost)) and metric_tensor_fn is None:
raise ValueError(
"The objective function must either be encoded as a single QNode or "
"an ExpvalCost object for the natural gradient to be automatically computed. "
"Otherwise, metric_tensor_fn must be explicitly provided to the optimizer."
)
if recompute_tensor or self.metric_tensor is None:
if metric_tensor_fn is None:
metric_tensor_fn = qml.metric_tensor(qnode, approx=self.approx)
self.metric_tensor = metric_tensor_fn(*args, **kwargs)
self.metric_tensor += self.lam * np.identity(
self.metric_tensor.shape[0])
g, forward = self.compute_grad(qnode, args, kwargs, grad_fn=grad_fn)
new_args = np.array(self.apply_grad(g, args), requires_grad=True)
if forward is None:
forward = qnode(*args, **kwargs)
# unwrap from list if one argument, cleaner return
if len(new_args) == 1:
return new_args[0], forward
return new_args, forward
def test_phase_shift(self, tol):
"""Test phase shift is correct"""
# test identity for theta=0
assert np.allclose(Rphi(0), np.identity(2), atol=tol, rtol=0)
# test arbitrary phase shift
phi = 0.5432
expected = np.array([[1, 0], [0, np.exp(1j * phi)]])
assert np.allclose(Rphi(phi), expected, atol=tol, rtol=0)
def test_phase_shift(self):
"""Test phase shift is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(Rphi(0), np.identity(2), delta=self.tol)
# test arbitrary phase shift
phi = 0.5432
expected = np.array([[1, 0], [0, np.exp(1j * phi)]])
self.assertAllAlmostEqual(Rphi(phi), expected, delta=self.tol)
def test_z_rotation(self, tol):
"""Test z rotation is correct"""
# test identity for theta=0
assert np.allclose(Rotz(0), np.identity(2), atol=tol, rtol=0)
# test identity for theta=pi/2
expected = np.diag(np.exp([-1j * np.pi / 4, 1j * np.pi / 4]))
assert np.allclose(Rotz(np.pi / 2), expected, atol=tol, rtol=0)
# test identity for theta=pi
assert np.allclose(Rotz(np.pi), -1j * Z, atol=tol, rtol=0)
def test_y_rotation(self, tol):
"""Test y rotation is correct"""
# test identity for theta=0
assert np.allclose(Roty(0), np.identity(2), atol=tol, rtol=0)
# test identity for theta=pi/2
expected = np.array([[1, -1], [1, 1]]) / np.sqrt(2)
assert np.allclose(Roty(np.pi / 2), expected, atol=tol, rtol=0)
# test identity for theta=pi
expected = np.array([[0, -1], [1, 0]])
assert np.allclose(Roty(np.pi), expected, atol=tol, rtol=0)
def test_z_rotation(self):
"""Test z rotation is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(Rotz(0), np.identity(2), delta=self.tol)
# test identity for theta=pi/2
expected = np.diag(np.exp([-1j * np.pi / 4, 1j * np.pi / 4]))
self.assertAllAlmostEqual(Rotz(np.pi / 2), expected, delta=self.tol)
# test identity for theta=pi
self.assertAllAlmostEqual(Rotz(np.pi), -1j * Z, delta=self.tol)
def test_C_z_rotation(self, tol):
"""Test controlled z rotation is correct"""
# test identity for theta=0
assert np.allclose(CRotz(0), np.identity(4), atol=tol, rtol=0)
# test identity for theta=pi/2
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, np.exp(-1j * np.pi / 4), 0], [0, 0, 0, np.exp(1j * np.pi / 4)]])
assert np.allclose(CRotz(np.pi / 2), expected, atol=tol, rtol=0)
# test identity for theta=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, -1j, 0], [0, 0, 0, 1j]])
assert np.allclose(CRotz(np.pi), expected, atol=tol, rtol=0)
def test_C_y_rotation(self, tol):
"""Test controlled y rotation is correct"""
# test identity for theta=0
assert np.allclose(CRoty(0), np.identity(4), atol=tol, rtol=0)
# test identity for theta=pi/2
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1/np.sqrt(2), -1/np.sqrt(2)], [0, 0, 1/np.sqrt(2), 1/np.sqrt(2)]])
assert np.allclose(CRoty(np.pi / 2), expected, atol=tol, rtol=0)
# test identity for theta=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1], [0, 0, 1, 0]])
assert np.allclose(CRoty(np.pi), expected, atol=tol, rtol=0)
def test_C_y_rotation(self):
"""Test controlled y rotation is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(CRoty(0), np.identity(4), delta=self.tol)
# test identity for theta=pi/2
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1/np.sqrt(2), -1/np.sqrt(2)], [0, 0, 1/np.sqrt(2), 1/np.sqrt(2)]])
self.assertAllAlmostEqual(CRoty(np.pi / 2), expected, delta=self.tol)
# test identity for theta=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 0, -1], [0, 0, 1, 0]])
self.assertAllAlmostEqual(CRoty(np.pi), expected, delta=self.tol)
def test_y_rotation(self):
"""Test y rotation is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(Roty(0), np.identity(2), delta=self.tol)
# test identity for theta=pi/2
expected = np.array([[1, -1], [1, 1]]) / np.sqrt(2)
self.assertAllAlmostEqual(Roty(np.pi / 2), expected, delta=self.tol)
# test identity for theta=pi
expected = np.array([[0, -1], [1, 0]])
self.assertAllAlmostEqual(Roty(np.pi), expected, delta=self.tol)
def test_C_z_rotation(self):
"""Test controlled z rotation is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(CRotz(0), np.identity(4), delta=self.tol)
# test identity for theta=pi/2
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, np.exp(-1j * np.pi / 4), 0], [0, 0, 0, np.exp(1j * np.pi / 4)]])
self.assertAllAlmostEqual(CRotz(np.pi / 2), expected, delta=self.tol)
# test identity for theta=pi
expected = np.array([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, -1j, 0], [0, 0, 0, 1j]])
self.assertAllAlmostEqual(CRotz(np.pi), expected, delta=self.tol)
def reference(*x):
"""reference circuit"""
if callable(qop):
# if the default.qubit is an operation accepting parameters,
# initialise it using the parameters generated above.
O = qop(*x)
else:
# otherwise, the operation is simply an array.
O = qop
# calculate the expected output
out_state = np.kron(Rotx(a) @ np.array([1, 0]), np.array([1, 0]))
expectation = out_state.conj() @ np.kron(O, np.identity(2)) @ out_state
return expectation
def GenHamiltonian(self):
'''
Define and diagonalize chains hamiltonian
'''
# Define Pauli operators
PauliX = np.array([
[0, 1],
[1, 0]
])
PauliY = np.array([
[0, -1j],
[1j, 0]
])
PauliZ = np.array([
[1, 0],
[0, -1]
])
PauliOps = [PauliX, PauliY, PauliZ]
# Definition of two-qubit Hamiltonian
Hij = np.sum(Jint * np.kron(Pauli, Pauli)
for Jint, Pauli in zip(self.ExchangeIntegrals, PauliOps))
# Definition of one-qubit Hamiltonian
Hi = np.sum(hcomp * Pauli
for hcomp, Pauli in zip(self.ExternalField, PauliOps))
# Definition of Chain Hamiltonian
Hchain = np.sum(
np.kron(np.identity(2**idx),
np.kron(Hij, np.identity(2**(self.num_spins-(idx + 2))))) +
np.kron(np.identity(2**idx),
np.kron(Hi, np.identity(2**(self.num_spins-(idx + 1)))))
for idx in range(self.num_spins-1)
) + np.kron(np.identity(2**(self.num_spins - 1)), Hi)
# Diagonalization of Hamiltonian
self.HamMatEnergies, self.HamMatEstates = np.linalg.eig(Hchain)
# Storing system hamiltonian
self.SysHamiltonian = qml.Hermitian(
Hchain, wires=range(self.num_spins))
def test_arbitrary_rotation(self, tol):
"""Test arbitrary single qubit rotation is correct"""
# test identity for phi,theta,omega=0
assert np.allclose(Rot3(0, 0, 0), np.identity(2), atol=tol, rtol=0)
# expected result
def arbitrary_rotation(x, y, z):
"""arbitrary single qubit rotation"""
c = np.cos(y / 2)
s = np.sin(y / 2)
return np.array(
[
[np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],
[np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c],
]
)
a, b, c = 0.432, -0.152, 0.9234
assert np.allclose(Rot3(a, b, c), arbitrary_rotation(a, b, c), atol=tol, rtol=0)
def reference(*x):
"""reference circuit"""
if g == 'GaussianState':
return x[0][0]
if g == 'Displacement':
alpha = x[0]*np.exp(1j*x[1])
return (alpha+a).real*np.sqrt(2*hbar)
if 'State' in g:
mu, _ = qop(*x, hbar=hbar)
return mu[0]
S = qop(*x)
# calculate the expected output
if op.num_wires == 1:
S = block_diag(S, np.identity(2))[:, [0, 2, 1, 3]][[0, 2, 1, 3]]
return (S @ np.array([a.real, a.imag, 0, 0])*np.sqrt(2*hbar))[0]
def test_controlled_phase(self):
"""Test the CZ symplectic transform."""
self.logTestName()
s = 0.543
S = controlled_phase(s)
# test that S = R_2(pi/2) CX(s) R_2(pi/2)^\dagger
R2 = block_diag(np.identity(2), rotation(np.pi/2))[:, [0, 2, 1, 3]][[0, 2, 1, 3]]
expected = R2 @ controlled_addition(s) @ R2.conj().T
self.assertAllAlmostEqual(S, expected, delta=self.tol)
# test that S[x1, x2, p1, p2] -> [x1, x2, p1+sx2, p2+sx1]
x1 = 0.5432
x2 = -0.453
p1 = 0.154
p2 = -0.123
out = S @ np.array([x1, x2, p1, p2])*np.sqrt(2*hbar)
expected = np.array([x1, x2, p1+s*x2, p2+s*x1])*np.sqrt(2*hbar)
self.assertAllAlmostEqual(out, expected, delta=self.tol)
def reference(*x):
"""reference circuit"""
if callable(qop):
# if the default.qubit is an operation accepting parameters,
# initialise it using the parameters generated above.
O = qop(*x)
else:
# otherwise, the operation is simply an array.
O = qop
# calculate the expected output
if op.num_wires == 1 or g == "QubitUnitary":
out_state = np.kron(O @ np.array([1, 0]), np.array([1, 0]))
elif g == "QubitStateVector":
out_state = x[0]
elif g == "BasisState":
out_state = np.array([0, 0, 0, 1])
else:
out_state = O @ dev._state
expectation = out_state.conj() @ np.kron(X, np.identity(2)) @ out_state
return expectation
def test_arbitrary_rotation(self):
"""Test arbitrary single qubit rotation is correct"""
self.logTestName()
# test identity for theta=0
self.assertAllAlmostEqual(Rot3(0, 0, 0), np.identity(2), delta=self.tol)
# expected result
def arbitrary_rotation(x, y, z):
"""arbitrary single qubit rotation"""
c = np.cos(y / 2)
s = np.sin(y / 2)
return np.array(
[
[np.exp(-0.5j * (x + z)) * c, -np.exp(0.5j * (x - z)) * s],
[np.exp(-0.5j * (x - z)) * s, np.exp(0.5j * (x + z)) * c],
]
)
a, b, c = 0.432, -0.152, 0.9234
self.assertAllAlmostEqual(
Rot3(a, b, c), arbitrary_rotation(a, b, c), delta=self.tol
)
# Applying this, we can see that
#
# .. math::
#
# H = 4 + 2I\otimes X + 4I \otimes Z - X\otimes X + 5 Y\otimes Y + 2Z\otimes X.
#
# To perform "doubly stochastic" gradient descent, we simply apply the stochastic
# gradient descent approach from above, but in addition also uniformly sample
# a subset of the terms for the Hamiltonian expectation at each optimization step.
# This inserts another element of stochasticity into the system---all the while
# convergence continues to be guaranteed!
#
# Let's create a QNode that randomly samples a single term from the above
# Hamiltonian as the observable to be measured.
I = np.identity(2)
X = np.array([[0, 1], [1, 0]])
Y = np.array([[0, -1j], [1j, 0]])
Z = np.array([[1, 0], [0, -1]])
terms = np.array([
2 * np.kron(I, X), 4 * np.kron(I, Z), -np.kron(X, X), 5 * np.kron(Y, Y),
2 * np.kron(Z, X)
])
@qml.qnode(dev_stochastic)
def circuit(params, n=None):
StronglyEntanglingLayers(weights=params, wires=[0, 1])
idx = np.random.choice(np.arange(5), size=n, replace=False)
A = np.sum(terms[idx], axis=0)
class DummyDevice(DefaultGaussian):
"""Dummy device to allow Kerr operations"""
_operation_map = DefaultGaussian._operation_map.copy()
_operation_map['Kerr'] = lambda *x, **y: np.identity(2)
# --- Sanity check ---
# @qml.qnode(device)
# def qft_inv_test(input):
# prepare_state(input)
# qft()
# iqft()
#
# basis_obs = qml.Hermitian(np.diag(range(2 ** n_qubits)), wires=range(n_qubits))
# return qml.sample(basis_obs)
#
# def test_qft(input):
# samples = qft_inv_test(input=input).astype(int)
# q_probs = np.bincount(samples, minlength=2 ** n_qubits) / n_shots
# return q_probs
#
I16 = np.identity(2**4)
# for i in range(2**4):
# q_probs = test_qft(I16[i])
# print(q_probs)
# --- Optimize: state -> QFT (using U) -> QFT (traditional) -> state
def eigenvalues2basisvector(eigenvalues):
n_basis_vector = sum(
[2**n if i == -1 else 0 for n, i in enumerate(reversed(eigenvalues))])
return [
*([0] * n_basis_vector), 1, *([0] * (2**n_qubits - n_basis_vector - 1))
]
##############################################################################
# Comparison of quantum and classical results
# -------------------------------------------
#
# Since the specific problem considered in this tutorial has a small size, we can also
# solve it in a classical way and then compare the results with our quantum solution.
#
##############################################################################
# Classical algorithm
# ^^^^^^^^^^^^^^^^^^^
# To solve the problem in a classical way, we use the explicit matrix representation in
# terms of numerical NumPy arrays.
Id = np.identity(2)
Z = np.array([[1, 0], [0, -1]])
X = np.array([[0, 1], [1, 0]])
A_0 = np.identity(8)
A_1 = np.kron(np.kron(X, Z), Id)
A_2 = np.kron(np.kron(X, Id), Id)
A_num = c[0] * A_0 + c[1] * A_1 + c[2] * A_2
b = np.ones(8) / np.sqrt(8)
##############################################################################
# We can print the explicit values of :math:`A` and :math:`b`:
print("A = \n", A_num)
print("b = \n", b)
def test_apply(self):
"""Test the application of gates to a state"""
self.logTestName()
# loop through all supported operations
for gate_name, fn in self.dev._operation_map.items():
log.debug("\tTesting %s gate...", gate_name)
self.dev.reset()
# start in the displaced squeezed state
alpha = 0.542 + 0.123j
a = abs(alpha)
phi_a = np.angle(alpha)
r = 0.652
phi_r = -0.124
self.dev.apply('DisplacedSqueezedState',
wires=[0],
par=[a, phi_a, r, phi_r])
self.dev.apply('DisplacedSqueezedState',
wires=[1],
par=[a, phi_a, r, phi_r])
# get the equivalent pennylane operation class
op = qml.ops.__getattribute__(gate_name)
# the list of wires to apply the operation to
w = list(range(op.num_wires))
if op.par_domain == 'A':
# the parameter is an array
if gate_name == 'GaussianState':
p = [
np.array([0.432, 0.123, 0.342, 0.123]),
np.diag([0.5234] * 4)
]
w = list(range(2))
expected_out = p
elif gate_name == 'Interferometer':
w = list(range(2))
p = [U]
S = fn(*p)
expected_out = S @ self.dev._state[0], S @ self.dev._state[
1] @ S.T
else:
# the parameter is a float
p = [0.432423, -0.12312, 0.324, 0.751][:op.num_params]
if gate_name == 'Displacement':
alpha = p[0] * np.exp(1j * p[1])
state = self.dev._state
mu = state[0].copy()
mu[w[0]] += alpha.real * np.sqrt(2 * hbar)
mu[w[0] + 2] += alpha.imag * np.sqrt(2 * hbar)
expected_out = mu, state[1]
elif 'State' in gate_name:
mu, cov = fn(*p, hbar=hbar)
expected_out = self.dev._state
expected_out[0][[w[0], w[0] + 2]] = mu
ind = np.concatenate(
[np.array([w[0]]),
np.array([w[0]]) + 2])
rows = ind.reshape(-1, 1)
cols = ind.reshape(1, -1)
expected_out[1][rows, cols] = cov
else:
# if the default.gaussian is an operation accepting parameters,
# initialise it using the parameters generated above.
S = fn(*p)
# calculate the expected output
if op.num_wires == 1:
# reorder from symmetric ordering to xp-ordering
S = block_diag(
S, np.identity(2))[:, [0, 2, 1, 3]][[0, 2, 1, 3]]
expected_out = S @ self.dev._state[0], S @ self.dev._state[
1] @ S.T
self.dev.apply(gate_name, wires=w, par=p)
# verify the device is now in the expected state
self.assertAllAlmostEqual(self.dev._state[0],
expected_out[0],
delta=self.tol)
self.assertAllAlmostEqual(self.dev._state[1],
expected_out[1],
delta=self.tol)
