def test_var(self, shots, qvm, compiler):
"""Tests for variance calculation"""
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
phi = 0.543
theta = 0.6543
with qml.tape.QuantumTape() as tape:
qml.RX(phi, wires=[0])
qml.RY(theta, wires=[0])
O1 = qml.var(qml.PauliZ(wires=[0]))
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
var = np.array([dev.var(O1.obs)])
expected = 0.25 * (3 - np.cos(2 * theta) -
2 * np.cos(theta)**2 * np.cos(2 * phi))
self.assertAlmostEqual(var, expected, delta=3 / np.sqrt(shots))
def test_var(self, shots, qvm, compiler):
"""Tests for variance calculation"""
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
phi = 0.543
theta = 0.6543
O1 = qml.var(qml.PauliZ(wires=[0]))
circuit_graph = CircuitGraph([qml.RX(phi, wires=[0]), qml.RY(theta, wires=[0])] + [O1], {}, dev.wires)
# test correct variance for <Z> of a rotated state
dev.apply(circuit_graph.operations, rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
var = np.array([dev.var(O1)])
expected = 0.25 * (3 - np.cos(2 * theta) - 2 * np.cos(theta) ** 2 * np.cos(2 * phi))
self.assertAlmostEqual(var, expected, delta=3 / np.sqrt(shots))
def test_pauliz_expectation(self, shots, qvm, compiler):
"""Test that PauliZ expectation value is correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device='2q-qvm', shots=shots)
dev.apply('RX', wires=[0], par=[theta])
dev.apply('RX', wires=[1], par=[phi])
dev.apply('CNOT', wires=[0, 1], par=[])
O = qml.expval.PauliZ
name = 'PauliZ'
dev._expval_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = dev.pre_expval()
res = np.array([dev.expval(name, [0], []), dev.expval(name, [1], [])])
# below are the analytic expectation values for this circuit
self.assertAllAlmostEqual(res, np.array([np.cos(theta), np.cos(theta)*np.cos(phi)]), delta=3/np.sqrt(shots))
def test_identity_expectation(self, shots, qvm, compiler):
"""Test that identity expectation value (i.e. the trace) is 1"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device='2q-qvm', shots=shots)
dev.apply('RX', wires=[0], par=[theta])
dev.apply('RX', wires=[1], par=[phi])
dev.apply('CNOT', wires=[0, 1], par=[])
O = qml.expval.qubit.Identity
name = 'Identity'
dev._expval_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
res = dev.pre_expval()
res = np.array([dev.expval(name, [0], []), dev.expval(name, [1], [])])
# below are the analytic expectation values for this circuit (trace should always be 1)
self.assertAllAlmostEqual(res, np.array([1, 1]), delta=3/np.sqrt(shots))
def test_sample_values(self, qvm, tol):
"""Tests if the samples returned by sample have
the correct values
"""
dev = plf.QVMDevice(device="1q-qvm", shots=10)
O1 = qml.expval(qml.PauliZ(wires=[0]))
circuit_graph = CircuitGraph([qml.RX(1.5708, wires=[0])] + [O1], {})
dev.apply(circuit_graph.operations,
rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
s1 = dev.sample(O1)
# s1 should only contain 1 and -1
self.assertAllAlmostEqual(s1**2, 1, delta=tol)
self.assertAllAlmostEqual(s1, 1 - 2 * dev._samples[:, 0], delta=tol)
def test_sample_values_hermitian(self, qvm, tol):
"""Tests if the samples of a Hermitian observable returned by sample have
the correct values
"""
theta = 0.543
shots = 1_000_000
A = np.array([[1, 2j], [-2j, 0]])
dev = plf.QVMDevice(device="1q-qvm", shots=shots)
O1 = qml.sample(qml.Hermitian(A, wires=[0]))
circuit_graph = CircuitGraph([qml.RX(theta, wires=[0])] + [O1], {},
dev.wires)
dev.apply(circuit_graph.operations,
rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
s1 = dev.sample(O1)
# s1 should only contain the eigenvalues of
# the hermitian matrix
eigvals = np.linalg.eigvalsh(A)
assert np.allclose(sorted(list(set(s1))),
sorted(eigvals),
atol=tol,
rtol=0)
# the analytic mean is 2*sin(theta)+0.5*cos(theta)+0.5
assert np.allclose(np.mean(s1),
2 * np.sin(theta) + 0.5 * np.cos(theta) + 0.5,
atol=0.1,
rtol=0)
# the analytic variance is 0.25*(sin(theta)-4*cos(theta))^2
assert np.allclose(np.var(s1),
0.25 * (np.sin(theta) - 4 * np.cos(theta))**2,
atol=0.1,
rtol=0)
def test_hermitian_expectation(self, shots, qvm, compiler):
"""Test that arbitrary Hermitian expectation values are correct.
As the results coming from the qvm are stochastic, a constraint of 3 out of 5 runs was added.
"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
O1 = qml.expval(qml.Hermitian(H, wires=[0]))
O2 = qml.expval(qml.Hermitian(H, wires=[1]))
circuit_graph = CircuitGraph(
[
qml.RY(theta, wires=[0]),
qml.RY(phi, wires=[1]),
qml.CNOT(wires=[0, 1])
] + [O1, O2],
{},
)
dev.apply(circuit_graph.operations,
rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1), dev.expval(O2)])
# below are the analytic expectation values for this circuit with arbitrary
# Hermitian observable H
a = H[0, 0]
re_b = H[0, 1].real
d = H[1, 1]
ev1 = ((a - d) * np.cos(theta) +
2 * re_b * np.sin(theta) * np.sin(phi) + a + d) / 2
ev2 = ((a - d) * np.cos(theta) * np.cos(phi) + 2 * re_b * np.sin(phi) +
a + d) / 2
expected = np.array([ev1, ev2])
self.assertAllAlmostEqual(res, expected, delta=4 / np.sqrt(shots))
def test_sample_values_hermitian_multi_qubit(self, qvm, tol):
"""Tests if the samples of a multi-qubit Hermitian observable returned by sample have
the correct values
"""
theta = 0.543
shots = 100_000
A = np.array([
[1, 2j, 1 - 2j, 0.5j],
[-2j, 0, 3 + 4j, 1],
[1 + 2j, 3 - 4j, 0.75, 1.5 - 2j],
[-0.5j, 1, 1.5 + 2j, -1],
])
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
with qml.tape.QuantumTape() as tape:
qml.RX(theta, wires=[0])
qml.RY(2 * theta, wires=[1])
qml.CNOT(wires=[0, 1])
O1 = qml.sample(qml.Hermitian(A, wires=[0, 1]))
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
s1 = dev.sample(O1.obs)
# s1 should only contain the eigenvalues of
# the hermitian matrix
eigvals = np.linalg.eigvalsh(A)
assert np.allclose(sorted(list(set(s1))),
sorted(eigvals),
atol=tol,
rtol=0)
# make sure the mean matches the analytic mean
expected = (88 * np.sin(theta) + 24 * np.sin(2 * theta) -
40 * np.sin(3 * theta) + 5 * np.cos(theta) -
6 * np.cos(2 * theta) + 27 * np.cos(3 * theta) + 6) / 32
assert np.allclose(np.mean(s1), expected, atol=0.1, rtol=0)
def test_var(self, shots):
"""Tests for variance calculation"""
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
phi = 0.543
theta = 0.6543
# test correct variance for <Z> of a rotated state
dev.apply("RX", wires=[0], par=[phi])
dev.apply("RY", wires=[0], par=[theta])
O = qml.PauliZ
name = "PauliZ"
dev._obs_queue = [O(wires=[0], do_queue=False)]
dev.pre_measure()
var = dev.var(name, [0], [])
expected = 0.25 * (3 - np.cos(2 * theta) - 2 * np.cos(theta) ** 2 * np.cos(2 * phi))
self.assertAlmostEqual(var, expected, delta=3 / np.sqrt(shots))
def test_pauliy_expectation(self, shots, qvm, compiler):
"""Test that PauliY expectation value is correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
dev.apply("RX", wires=[0], par=[theta])
dev.apply("RX", wires=[1], par=[phi])
dev.apply("CNOT", wires=[0, 1], par=[])
O = qml.PauliY
name = "PauliY"
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
dev.pre_measure()
# below are the analytic expectation values for this circuit
res = np.array([dev.expval(name, [0], []), dev.expval(name, [1], [])])
self.assertAllAlmostEqual(
res, np.array([0, -np.cos(theta) * np.sin(phi)]), delta=3 / np.sqrt(shots)
)
def test_hadamard_expectation(self, shots, qvm, compiler):
"""Test that Hadamard expectation value is correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
dev.apply("RY", wires=[0], par=[theta])
dev.apply("RY", wires=[1], par=[phi])
dev.apply("CNOT", wires=[0, 1], par=[])
O = qml.Hadamard
name = "Hadamard"
dev._obs_queue = [O(wires=[0], do_queue=False), O(wires=[1], do_queue=False)]
dev.pre_measure()
res = np.array([dev.expval(name, [0], []), dev.expval(name, [1], [])])
# below are the analytic expectation values for this circuit
expected = np.array(
[np.sin(theta) * np.sin(phi) + np.cos(theta), np.cos(theta) * np.cos(phi) + np.sin(phi)]
) / np.sqrt(2)
self.assertAllAlmostEqual(res, expected, delta=3 / np.sqrt(shots))
def test_identity_expectation(self, shots, qvm, compiler):
"""Test that identity expectation value (i.e. the trace) is 1"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
O1 = qml.expval(qml.Identity(wires=[0]))
O2 = qml.expval(qml.Identity(wires=[1]))
circuit_graph = CircuitGraph(
[qml.RX(theta, wires=[0]), qml.RX(phi, wires=[1]), qml.CNOT(wires=[0, 1])], [O1, O2], dev.wires
)
dev.apply(circuit_graph.operations, rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1), dev.expval(O2)])
# below are the analytic expectation values for this circuit (trace should always be 1)
self.assertAllAlmostEqual(res, np.array([1, 1]), delta=3 / np.sqrt(shots))
def test_multi_mode_hermitian_expectation(self, shots, qvm, compiler):
"""Test that arbitrary multi-mode Hermitian expectation values are correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=10 * shots)
dev.apply("RY", wires=[0], par=[theta])
dev.apply("RY", wires=[1], par=[phi])
dev.apply("CNOT", wires=[0, 1], par=[])
O = qml.Hermitian
name = "Hermitian"
A = np.array(
[
[-6, 2 + 1j, -3, -5 + 2j],
[2 - 1j, 0, 2 - 1j, -5 + 4j],
[-3, 2 + 1j, 0, -4 + 3j],
[-5 - 2j, -5 - 4j, -4 - 3j, -6],
]
)
dev._obs_queue = [O(A, wires=[0, 1], do_queue=False)]
dev.pre_measure()
res = np.array([dev.expval(name, [0, 1], [A])])
# below is the analytic expectation value for this circuit with arbitrary
# Hermitian observable A
expected = 0.5 * (
6 * np.cos(theta) * np.sin(phi)
- np.sin(theta) * (8 * np.sin(phi) + 7 * np.cos(phi) + 3)
- 2 * np.sin(phi)
- 6 * np.cos(phi)
- 6
)
self.assertAllAlmostEqual(res, expected, delta=4 / np.sqrt(shots))
def test_pauliy_expectation(self, shots, qvm, compiler):
"""Test that PauliY expectation value is correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
with qml.tape.QuantumTape() as tape:
qml.RX(theta, wires=[0])
qml.RX(phi, wires=[1])
qml.CNOT(wires=[0, 1])
O1 = qml.expval(qml.PauliY(wires=[0]))
O2 = qml.expval(qml.PauliY(wires=[1]))
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1.obs), dev.expval(O2.obs)])
# below are the analytic expectation values for this circuit
self.assertAllAlmostEqual(res,
np.array([0, -np.cos(theta) * np.sin(phi)]),
delta=3 / np.sqrt(shots))
def test_paulix_expectation(self, shots):
"""Test that PauliX expectation value is correct"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-pyqvm", shots=shots)
O1 = qml.expval(qml.PauliX(wires=[0]))
O2 = qml.expval(qml.PauliX(wires=[1]))
circuit_graph = CircuitGraph(
[qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])] + [O1, O2],
{},
)
dev.apply(circuit_graph.operations, rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1), dev.expval(O2)])
# below are the analytic expectation values for this circuit
self.assertAllAlmostEqual(
res, np.array([np.sin(theta) * np.sin(phi), np.sin(phi)]), delta=3 / np.sqrt(shots)
)
def test_multi_qubit_hermitian_expectation(self, shots, qvm, compiler):
"""Test that arbitrary multi-qubit Hermitian expectation values are correct"""
theta = np.random.random()
phi = np.random.random()
A = np.array(
[
[-6, 2 + 1j, -3, -5 + 2j],
[2 - 1j, 0, 2 - 1j, -5 + 4j],
[-3, 2 + 1j, 0, -4 + 3j],
[-5 - 2j, -5 - 4j, -4 - 3j, -6],
]
)
dev = plf.QVMDevice(device="2q-pyqvm", shots=10 * shots)
O1 = qml.expval(qml.Hermitian(A, wires=[0, 1]))
circuit_graph = CircuitGraph(
[qml.RY(theta, wires=[0]), qml.RY(phi, wires=[1]), qml.CNOT(wires=[0, 1])] + [O1], {}
)
dev.apply(circuit_graph.operations, rotations=circuit_graph.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1)])
# below is the analytic expectation value for this circuit with arbitrary
# Hermitian observable A
expected = 0.5 * (
6 * np.cos(theta) * np.sin(phi)
- np.sin(theta) * (8 * np.sin(phi) + 7 * np.cos(phi) + 3)
- 2 * np.sin(phi)
- 6 * np.cos(phi)
- 6
)
self.assertAllAlmostEqual(res, expected, delta=6 / np.sqrt(shots))
def test_apply(self, op, apply_unitary, shots, qvm, compiler):
"""Test the application of gates to a state"""
dev = plf.QVMDevice(device="3q-qvm",
shots=shots,
parametric_compilation=False)
obs = qml.expval(qml.PauliZ(0))
if op.name == "QubitUnitary":
state = apply_unitary(U, 3)
elif op.name == "BasisState":
state = np.array([0, 0, 0, 0, 0, 0, 0, 1])
elif op.name == "CPHASE":
state = apply_unitary(test_operation_map["CPHASE"](0.432, 2), 3)
elif op.name == "ISWAP":
state = apply_unitary(test_operation_map["ISWAP"], 3)
elif op.name == "PSWAP":
state = apply_unitary(test_operation_map["PSWAP"](0.432), 3)
else:
state = apply_unitary(op.matrix, 3)
with qml.tape.QuantumTape() as tape:
qml.apply(op)
obs
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = dev.expval(obs.obs)
expected = np.vdot(state, np.kron(np.kron(Z, I), I) @ state)
# verify the device is now in the expected state
# Note we have increased the tolerance here, since we are only
# performing 1024 shots.
self.assertAllAlmostEqual(res, expected, delta=3 / np.sqrt(shots))
def test_identity_expectation(self, shots, qvm, compiler):
"""Test that identity expectation value (i.e. the trace) is 1"""
theta = 0.432
phi = 0.123
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
with qml.tape.QuantumTape() as tape:
qml.RX(theta, wires=[0])
qml.RX(phi, wires=[1])
qml.CNOT(wires=[0, 1])
O1 = qml.expval(qml.Identity(wires=[0]))
O2 = qml.expval(qml.Identity(wires=[1]))
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = np.array([dev.expval(O1.obs), dev.expval(O2.obs)])
# below are the analytic expectation values for this circuit (trace should always be 1)
self.assertAllAlmostEqual(res,
np.array([1, 1]),
delta=3 / np.sqrt(shots))
def test_timeout_set_correctly(self, shots):
"""Test that the timeout attrbiute for the QuantumComputer stored by the QVMDevice
is set correctly when passing a value as keyword argument"""
device = np.random.choice(TEST_QPU_LATTICES)
dev = plf.QVMDevice(device=device, shots=shots, timeout=100)
assert dev.qc.compiler.client.timeout == 100
def test_apply(self, gate, apply_unitary, shots, qvm, compiler):
"""Test the application of gates"""
dev = plf.QVMDevice(device="3q-qvm",
shots=shots,
parametric_compilation=False)
try:
# get the equivalent pennylane operation class
op = getattr(qml.ops, gate)
except AttributeError:
# get the equivalent pennylane-forest operation class
op = getattr(plf, gate)
# 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 == "QubitUnitary":
p = np.array(U)
w = [0]
state = apply_unitary(U, 3)
elif gate == "BasisState":
p = np.array([1, 1, 1])
state = np.array([0, 0, 0, 0, 0, 0, 0, 1])
w = list(range(dev.num_wires))
with qml.tape.QuantumTape() as tape:
op(p, wires=w)
obs = qml.expval(qml.PauliZ(0))
else:
p = [0.432_423, 2, 0.324][:op.num_params]
fn = test_operation_map[gate]
if callable(fn):
# if the default.qubit is an operation accepting parameters,
# initialise it using the parameters generated above.
O = fn(*p)
else:
# otherwise, the operation is simply an array.
O = fn
# calculate the expected output
state = apply_unitary(O, 3)
# Creating the tape using a parametrized operation
if p:
with qml.tape.QuantumTape() as tape:
op(*p, wires=w)
obs = qml.expval(qml.PauliZ(0))
# Creating the tape using an operation that take no parameters
else:
with qml.tape.QuantumTape() as tape:
op(wires=w)
obs = qml.expval(qml.PauliZ(0))
dev.apply(tape.operations, rotations=tape.diagonalizing_gates)
dev._samples = dev.generate_samples()
res = dev.expval(obs.obs)
expected = np.vdot(state, np.kron(np.kron(Z, I), I) @ state)
# verify the device is now in the expected state
# Note we have increased the tolerance here, since we are only
# performing 1024 shots.
self.assertAllAlmostEqual(res, expected, delta=3 / np.sqrt(shots))
def test_raise_error_if_shots_is_not_positive(self, shots):
"""Test that instantiating a QVMDevice if the number of shots is not a postivie
integer raises an error"""
with pytest.raises(ValueError, match="Number of shots must be a positive integer."):
dev = plf.QVMDevice(device="2q-qvm", shots=shots)
def test_raise_error_if_analytic_true(self, shots):
"""Test that instantiating a QVMDevice in analytic=True mode raises an error"""
with pytest.raises(
ValueError,
match="QVM device cannot be run in analytic=True mode."):
dev = plf.QVMDevice(device="2q-qvm", shots=shots, analytic=True)
def test_raise_error_if_shots_is_none(self, shots):
"""Test that instantiating a QVMDevice to be used for analytic computations raises an error"""
with pytest.raises(
ValueError,
match="QVM device cannot be used for analytic computations."):
dev = plf.QVMDevice(device="2q-qvm", shots=None)
