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test_adjoint_jacobian.py
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# Copyright 2018-2023 Xanadu Quantum Technologies Inc.
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Unit tests for the :mod:`pennylane_lightning_gpu.LightningGPU` device (MPI).
"""
# pylint: disable=protected-access,cell-var-from-loop,c-extension-no-member
import itertools
import math
import pennylane as qml
import pytest
from conftest import LightningDevice as ld
from conftest import device_name
from mpi4py import MPI
from pennylane import QNode
from pennylane import numpy as np
from pennylane import qnode
from scipy.stats import unitary_group
if not ld._CPP_BINARY_AVAILABLE:
pytest.skip("No binary module found. Skipping.", allow_module_level=True)
I, X, Y, Z = (
np.eye(2),
qml.PauliX.compute_matrix(),
qml.PauliY.compute_matrix(),
qml.PauliZ.compute_matrix(),
)
# Tuple passed to distributed device ctor
# np.complex for data type and True or False
# for enabling batched_obs.
fixture_params = itertools.product(
[np.complex64, np.complex128],
[True, False],
)
@pytest.fixture(name="dev", params=fixture_params)
def fixture_dev(request):
"""Returns a PennyLane device."""
return qml.device(
device_name,
wires=8,
mpi=True,
c_dtype=request.param[0],
batch_obs=request.param[1],
)
def Rx(theta):
r"""One-qubit rotation about the x axis.
Args:
theta (float): rotation angle
Returns:
array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_x \theta/2}`
"""
return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * X
def Ry(theta):
r"""One-qubit rotation about the y axis.
Args:
theta (float): rotation angle
Returns:
array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_y \theta/2}`
"""
return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * Y
def Rz(theta):
r"""One-qubit rotation about the z axis.
Args:
theta (float): rotation angle
Returns:
array: unitary 2x2 rotation matrix :math:`e^{-i \sigma_z \theta/2}`
"""
return math.cos(theta / 2) * I + 1j * math.sin(-theta / 2) * Z
class TestAdjointJacobian: # pylint: disable=too-many-public-methods
"""Tests for the adjoint_jacobian method"""
def test_not_expval(self, dev):
"""Test if a QuantumFunctionError is raised for a tape with measurements that are not
expectation values"""
with qml.tape.QuantumTape() as tape:
qml.RX(0.1, wires=0)
qml.var(qml.PauliZ(0))
with pytest.raises(
qml.QuantumFunctionError, match="Adjoint differentiation method does not"
):
dev.adjoint_jacobian(tape)
with qml.tape.QuantumTape() as tape:
qml.RX(0.1, wires=0)
qml.state()
if device_name == "lightning.gpu":
message = "Adjoint differentiation does not support State measurements."
else:
message = "Adjoint differentiation method does not support measurement StateMP."
with pytest.raises(
qml.QuantumFunctionError,
match=message,
):
dev.adjoint_jacobian(tape)
def test_finite_shots_warns(self):
"""Tests warning raised when finite shots specified"""
dev = qml.device(device_name, wires=8, mpi=True, shots=1)
with qml.tape.QuantumTape() as tape:
qml.expval(qml.PauliZ(0))
with pytest.warns(
UserWarning,
match="Requested adjoint differentiation to be computed with finite shots.",
):
dev.adjoint_jacobian(tape)
def test_empty_measurements(self, dev):
"""Tests if an empty array is returned when the measurements of the tape is empty."""
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
jac = dev.adjoint_jacobian(tape)
assert len(jac) == 0
def test_unsupported_op(self, dev):
"""Test if a QuantumFunctionError is raised for an unsupported operation, i.e.,
multi-parameter operations that are not qml.Rot"""
with qml.tape.QuantumTape() as tape:
qml.CRot(0.1, 0.2, 0.3, wires=[0, 1])
qml.expval(qml.PauliZ(0))
with pytest.raises(
qml.QuantumFunctionError,
match="The CRot operation is not supported using the",
):
dev.adjoint_jacobian(tape)
def test_proj_unsupported(self, dev):
"""Test if a QuantumFunctionError is raised for a Projector observable"""
with qml.tape.QuantumTape() as tape:
qml.CRX(0.1, wires=[0, 1])
qml.expval(qml.Projector([0, 1], wires=[0, 1]))
with pytest.raises(
qml.QuantumFunctionError,
match="differentiation method does not support the Projector",
):
dev.adjoint_jacobian(tape)
with qml.tape.QuantumTape() as tape:
qml.CRX(0.1, wires=[0, 1])
qml.expval(qml.Projector([0], wires=[0]) @ qml.PauliZ(0))
with pytest.raises(
qml.QuantumFunctionError,
match="differentiation method does not support the Projector",
):
dev.adjoint_jacobian(tape)
@pytest.mark.parametrize("theta", np.linspace(-2 * np.pi, 2 * np.pi, 7))
@pytest.mark.parametrize("G", [qml.RX, qml.RY, qml.RZ])
@pytest.mark.parametrize("stateprep", [qml.QubitStateVector, qml.StatePrep])
def test_pauli_rotation_gradient(self, stateprep, G, theta, dev):
"""Tests that the automatic gradients of Pauli rotations are correct."""
random_state = np.array(
[0.43593284 - 0.02945156j, 0.40812291 + 0.80158023j], requires_grad=False
)
tape = qml.tape.QuantumScript(
[stateprep(random_state, 0), G(theta, 0)], [qml.expval(qml.PauliZ(0))]
)
tape.trainable_params = {1}
calculated_val = dev.adjoint_jacobian(tape)
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
# compare to finite differences
tapes, fn = qml.gradients.param_shift(tape)
numeric_val = fn(qml.execute(tapes, dev, None))
assert np.allclose(calculated_val, numeric_val, atol=tol, rtol=0)
@pytest.mark.parametrize("theta", np.linspace(-2 * np.pi, 2 * np.pi, 7))
@pytest.mark.parametrize("stateprep", [qml.QubitStateVector, qml.StatePrep])
def test_Rot_gradient(self, stateprep, theta, dev):
"""Tests that the device gradient of an arbitrary Euler-angle-parameterized gate is
correct."""
params = np.array([theta, theta**3, np.sqrt(2) * theta])
with qml.tape.QuantumTape() as tape:
stateprep(np.array([1.0, -1.0], requires_grad=False) / np.sqrt(2), wires=0)
qml.Rot(*params, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
calculated_val = dev.adjoint_jacobian(tape)
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
# compare to finite differences
tapes, fn = qml.gradients.param_shift(tape)
numeric_val = fn(qml.execute(tapes, dev, None))
assert np.allclose(calculated_val, numeric_val, atol=tol, rtol=0)
@pytest.mark.parametrize("par", [1, -2, 1.623, -0.051, 0]) # integers, floats, zero
def test_ry_gradient(self, par, tol, dev):
"""Test that the gradient of the RY gate matches the exact analytic formula."""
with qml.tape.QuantumTape() as tape:
qml.RY(par, wires=[0])
qml.expval(qml.PauliX(0))
tape.trainable_params = {0}
# gradients
exact = np.cos(par)
grad_A = dev.adjoint_jacobian(tape)
# different methods must agree
assert np.allclose(grad_A, exact, atol=tol, rtol=0)
def test_rx_gradient(self, tol, dev):
"""Test that the gradient of the RX gate matches the known formula."""
a = 0.7418
with qml.tape.QuantumTape() as tape:
qml.RX(a, wires=0)
qml.expval(qml.PauliZ(0))
# circuit jacobians
dev_jacobian = dev.adjoint_jacobian(tape)
expected_jacobian = -np.sin(a)
assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0)
def test_multiple_rx_gradient_pauliz(self, tol, dev):
"""Tests that the gradient of multiple RX gates in a circuit yields the correct result."""
params = np.array([np.pi, np.pi / 2, np.pi / 3])
with qml.tape.QuantumTape() as tape:
qml.RX(params[0], wires=0)
qml.RX(params[1], wires=1)
qml.RX(params[2], wires=2)
for idx in range(3):
qml.expval(qml.PauliZ(idx))
# circuit jacobians
dev_jacobian = dev.adjoint_jacobian(tape)
expected_jacobian = -np.diag(np.sin(params))
assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0)
def test_multiple_rx_gradient_hermitian(self, tol, dev):
"""Tests that the gradient of multiple RX gates in a circuit yields the correct result
with Hermitian observable
"""
params = np.array([np.pi, np.pi / 2, np.pi / 3])
with qml.tape.QuantumTape() as tape:
qml.RX(params[0], wires=0)
qml.RX(params[1], wires=1)
qml.RX(params[2], wires=2)
for idx in range(3):
qml.expval(qml.Hermitian([[1, 0], [0, -1]], wires=[idx]))
tape.trainable_params = {0, 1, 2}
# circuit jacobians
dev_jacobian = dev.adjoint_jacobian(tape)
expected_jacobian = -np.diag(np.sin(params))
assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0)
qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member
ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot}
def test_multiple_rx_gradient_expval_hermitian(self, tol, dev):
"""Tests that the gradient of multiple RX gates in a circuit yields the correct result
with Hermitian observable
"""
params = np.array([np.pi / 3, np.pi / 4, np.pi / 5])
with qml.tape.QuantumTape() as tape:
qml.RX(params[0], wires=0)
qml.RX(params[1], wires=1)
qml.RX(params[2], wires=2)
qml.expval(
qml.Hermitian(
[[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]],
wires=[0, 2],
)
)
tape.trainable_params = {0, 1, 2}
dev_jacobian = dev.adjoint_jacobian(tape)
expected_jacobian = np.array(
[
-np.sin(params[0]) * np.cos(params[2]),
0,
-np.cos(params[0]) * np.sin(params[2]),
]
)
assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0)
qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member
ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot}
def test_multiple_rx_gradient_expval_hamiltonian(self, tol, dev):
"""Tests that the gradient of multiple RX gates in a circuit yields the correct result
with Hermitian observable
"""
params = np.array([np.pi / 3, np.pi / 4, np.pi / 5])
ham = qml.Hamiltonian(
[1.0, 0.3, 0.3, 0.4],
[
qml.PauliX(0) @ qml.PauliX(1),
qml.PauliZ(0),
qml.PauliZ(1),
qml.Hermitian(
[[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, -1, 0], [0, 0, 0, 1]],
wires=[0, 2],
),
],
)
with qml.tape.QuantumTape() as tape:
qml.RX(params[0], wires=0)
qml.RX(params[1], wires=1)
qml.RX(params[2], wires=2)
qml.expval(ham)
tape.trainable_params = {0, 1, 2}
dev_jacobian = dev.adjoint_jacobian(tape)
expected_jacobian = (
0.3 * np.array([-np.sin(params[0]), 0, 0])
+ 0.3 * np.array([0, -np.sin(params[1]), 0])
+ 0.4
* np.array(
[
-np.sin(params[0]) * np.cos(params[2]),
0,
-np.cos(params[0]) * np.sin(params[2]),
]
)
)
assert np.allclose(dev_jacobian, expected_jacobian, atol=tol, rtol=0)
qubit_ops = [getattr(qml, name) for name in qml.ops._qubit__ops__] # pylint: disable=no-member
ops = {qml.RX, qml.RY, qml.RZ, qml.PhaseShift, qml.CRX, qml.CRY, qml.CRZ, qml.Rot}
@pytest.mark.parametrize("obs", [qml.PauliX, qml.PauliY])
@pytest.mark.parametrize(
"op",
[
qml.RX(0.4, wires=0),
qml.RY(0.6, wires=0),
qml.RZ(0.8, wires=0),
qml.CRX(1.0, wires=[0, 1]),
qml.CRY(2.0, wires=[0, 1]),
qml.CRZ(3.0, wires=[0, 1]),
qml.Rot(0.2, -0.1, 0.2, wires=0),
],
)
def test_gradients_pauliz(self, op, obs, dev):
"""Tests that the gradients of circuits match between the finite difference and device
methods."""
# op.num_wires and op.num_params must be initialized a priori
with qml.tape.QuantumTape() as tape:
qml.Hadamard(wires=0)
qml.RX(0.543, wires=0)
qml.CNOT(wires=[0, 1])
op # pylint: disable=pointless-statement
qml.Rot(1.3, -2.3, 0.5, wires=[0])
qml.RZ(-0.5, wires=0)
qml.adjoint(qml.RY(0.5, wires=1), lazy=False)
qml.CNOT(wires=[0, 1])
qml.expval(obs(wires=0))
qml.expval(qml.PauliZ(wires=1))
tape.trainable_params = set(range(1, 1 + op.num_params))
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
# pylint: disable=unnecessary-direct-lambda-call
grad_F = (lambda t, fn: fn(qml.execute(t, dev, None)))(*qml.gradients.param_shift(tape))
grad_D = dev.adjoint_jacobian(tape)
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
@pytest.mark.parametrize(
"op",
[
qml.RX(0.4, wires=0),
qml.RY(0.6, wires=0),
qml.RZ(0.8, wires=0),
qml.CRX(1.0, wires=[0, 1]),
qml.CRY(2.0, wires=[0, 1]),
qml.CRZ(3.0, wires=[0, 1]),
qml.Rot(0.2, -0.1, 0.2, wires=0),
],
)
def test_gradients_hermitian(self, op, dev):
"""Tests that the gradients of circuits match between the finite difference and device
methods."""
# op.num_wires and op.num_params must be initialized a priori
with qml.tape.QuantumTape() as tape:
qml.Hadamard(wires=0)
qml.RX(0.543, wires=0)
qml.CNOT(wires=[0, 1])
op.queue()
qml.Rot(1.3, -2.3, 0.5, wires=[0])
qml.RZ(-0.5, wires=0)
qml.adjoint(qml.RY(0.5, wires=1), lazy=False)
qml.CNOT(wires=[0, 1])
qml.expval(
qml.Hermitian(
[[0, 0, 1, 1], [0, 1, 2, 1], [1, 2, 1, 0], [1, 1, 0, 0]],
wires=[0, 1],
)
)
tape.trainable_params = set(range(1, 1 + op.num_params))
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
# pylint: disable=unnecessary-direct-lambda-call
grad_F = (lambda t, fn: fn(qml.execute(t, dev, None)))(*qml.gradients.param_shift(tape))
grad_D = dev.adjoint_jacobian(tape)
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
def test_gradient_gate_with_multiple_parameters_pauliz(self, dev):
"""Tests that gates with multiple free parameters yield correct gradients."""
x, y, z = [0.5, 0.3, -0.7]
tape = qml.tape.QuantumScript(
[
qml.RX(0.4, wires=[0]),
qml.Rot(x, y, z, wires=[0]),
qml.RY(-0.2, wires=[0]),
],
[qml.expval(qml.PauliZ(0))],
)
tape.trainable_params = {1, 2, 3}
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
grad_D = dev.adjoint_jacobian(tape)
tapes, fn = qml.gradients.param_shift(tape)
grad_F = fn(qml.execute(tapes, dev, None))
# gradient has the correct shape and every element is nonzero
assert len(grad_D) == 3
assert all(isinstance(v, np.ndarray) for v in grad_D)
assert np.count_nonzero(grad_D) == 3
# the different methods agree
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
def test_gradient_gate_with_multiple_parameters_hermitian(self, dev):
"""Tests that gates with multiple free parameters yield correct gradients."""
x, y, z = [0.5, 0.3, -0.7]
tape = qml.tape.QuantumScript(
[
qml.RX(0.4, wires=[0]),
qml.Rot(x, y, z, wires=[0]),
qml.RY(-0.2, wires=[0]),
],
[qml.expval(qml.Hermitian([[0, 1], [1, 1]], wires=0))],
)
tape.trainable_params = {1, 2, 3}
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
grad_D = dev.adjoint_jacobian(tape)
tapes, fn = qml.gradients.param_shift(tape)
grad_F = fn(qml.execute(tapes, dev, None))
# gradient has the correct shape and every element is nonzero
assert len(grad_D) == 3
assert all(isinstance(v, np.ndarray) for v in grad_D)
assert np.count_nonzero(grad_D) == 3
# the different methods agree
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
def test_gradient_gate_with_multiple_parameters_hamiltonian(self, dev):
"""Tests that gates with multiple free parameters yield correct gradients."""
x, y, z = [0.5, 0.3, -0.7]
ham = qml.Hamiltonian(
[1.0, 0.3, 0.3],
[qml.PauliX(0) @ qml.PauliX(1), qml.PauliZ(0), qml.PauliZ(1)],
)
tape = qml.tape.QuantumScript(
[
qml.RX(0.4, wires=[0]),
qml.Rot(x, y, z, wires=[0]),
qml.RY(-0.2, wires=[0]),
],
[qml.expval(ham)],
)
tape.trainable_params = {1, 2, 3}
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
grad_D = dev.adjoint_jacobian(tape)
tapes, fn = qml.gradients.param_shift(tape)
grad_F = fn(qml.execute(tapes, dev, None))
# gradient has the correct shape and every element is nonzero
assert len(grad_D) == 3
assert all(isinstance(v, np.ndarray) for v in grad_D)
assert np.count_nonzero(grad_D) == 3
# the different methods agree
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
def test_use_device_state(self, tol, dev):
"""Tests that when using the device state, the correct answer is still returned."""
x, y, z = [0.5, 0.3, -0.7]
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.Rot(x, y, z, wires=[0])
qml.RY(-0.2, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
dM1 = dev.adjoint_jacobian(tape)
qml.execute([tape], dev, None)
dM2 = dev.adjoint_jacobian(tape, use_device_state=True)
assert np.allclose(dM1, dM2, atol=tol, rtol=0)
def test_provide_starting_state(self, tol, dev):
"""Tests provides correct answer when provided starting state."""
comm = MPI.COMM_WORLD
x, y, z = [0.5, 0.3, -0.7]
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.Rot(x, y, z, wires=[0])
qml.RY(-0.2, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
dM1 = dev.adjoint_jacobian(tape)
if device_name == "lightning.gpu":
local_state_vector = dev.state
complex_type = np.complex128 if dev.R_DTYPE == np.float64 else np.complex64
state_vector = np.zeros(1 << 8).astype(complex_type)
comm.Allgather(local_state_vector, state_vector)
qml.execute([tape], dev, None)
dM2 = dev.adjoint_jacobian(tape, starting_state=state_vector)
assert np.allclose(dM1, dM2, atol=tol, rtol=0)
def test_provide_wrong_starting_state(self, dev):
"""Tests raise an exception when provided starting state mismatches."""
x, y, z = [0.5, 0.3, -0.7]
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.Rot(x, y, z, wires=[0])
qml.RY(-0.2, wires=[0])
qml.expval(qml.PauliZ(0))
tape.trainable_params = {1, 2, 3}
with pytest.raises(
qml.QuantumFunctionError,
match="The number of qubits of starting_state must be the same as",
):
dev.adjoint_jacobian(tape, starting_state=np.ones(7))
@pytest.mark.skipif(
device_name == "lightning.gpu",
reason="Adjoint differentiation does not support State measurements.",
)
def test_state_return_type(self, dev):
"""Tests raise an exception when the return type is State"""
with qml.tape.QuantumTape() as tape:
qml.RX(0.4, wires=[0])
qml.state()
tape.trainable_params = {0}
with pytest.raises(
qml.QuantumFunctionError,
match="Adjoint differentiation method does not support measurement StateMP.",
):
dev.adjoint_jacobian(tape)
class TestAdjointJacobianQNode:
"""Test QNode integration with the adjoint_jacobian method"""
@pytest.fixture(params=fixture_params)
def dev(self, request):
"""Returns a PennyLane device."""
return qml.device(
device_name,
wires=8,
mpi=True,
c_dtype=request.param[0],
batch_obs=request.param[1],
)
def test_finite_shots_warning(self):
"""Tests that a warning is raised when computing the adjoint diff on a device with finite shots"""
dev = qml.device(device_name, wires=8, mpi=True, shots=1)
with pytest.warns(
UserWarning,
match="Requested adjoint differentiation to be computed with finite shots.",
):
@qml.qnode(dev, diff_method="adjoint")
def circ(x):
qml.RX(x, wires=0)
return qml.expval(qml.PauliZ(0))
with pytest.warns(
UserWarning,
match="Requested adjoint differentiation to be computed with finite shots.",
):
qml.grad(circ)(0.1)
def test_qnode(self, mocker, dev):
"""Test that specifying diff_method allows the adjoint method to be selected"""
args = np.array([0.54, 0.1, 0.5], requires_grad=True)
def circuit(x, y, z):
qml.Hadamard(wires=0)
qml.RX(0.543, wires=0)
qml.CNOT(wires=[0, 1])
qml.Rot(x, y, z, wires=0)
qml.Rot(1.3, -2.3, 0.5, wires=[0])
qml.RZ(-0.5, wires=0)
qml.RY(0.5, wires=1)
qml.CNOT(wires=[0, 1])
return qml.expval(qml.PauliX(0) @ qml.PauliZ(1))
qnode1 = QNode(circuit, dev, diff_method="adjoint")
spy = mocker.spy(dev, "adjoint_jacobian")
grad_fn = qml.grad(qnode1)
grad_A = grad_fn(*args)
spy.assert_called()
h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
qnode2 = QNode(circuit, dev, diff_method="finite-diff", h=h)
grad_fn = qml.grad(qnode2)
grad_F = grad_fn(*args)
assert np.allclose(grad_A, grad_F, atol=tol, rtol=0)
thetas = np.linspace(-2 * np.pi, 2 * np.pi, 8)
@pytest.mark.parametrize("reused_p", thetas**3 / 19)
@pytest.mark.parametrize("other_p", thetas**2 / 1)
def test_fanout_multiple_params(
self, reused_p, other_p, tol, mocker, dev
): # pylint: disable=too-many-arguments
"""Tests that the correct gradient is computed for qnodes which
use the same parameter in multiple gates."""
def expZ(state):
return np.abs(state[0]) ** 2 - np.abs(state[1]) ** 2
extra_param = np.array(0.31, requires_grad=False)
@qnode(dev, diff_method="adjoint")
def cost(p1, p2):
qml.RX(extra_param, wires=[0])
qml.RY(p1, wires=[0])
qml.RZ(p2, wires=[0])
qml.RX(p1, wires=[0])
return qml.expval(qml.PauliZ(0))
zero_state = np.array([1.0, 0.0])
cost(reused_p, other_p)
spy = mocker.spy(dev, "adjoint_jacobian")
# analytic gradient
grad_fn = qml.grad(cost)
grad_D = grad_fn(reused_p, other_p)
spy.assert_called_once()
# manual gradient
grad_true0 = (
expZ(
Rx(reused_p) @ Rz(other_p) @ Ry(reused_p + np.pi / 2) @ Rx(extra_param) @ zero_state
)
- expZ(
Rx(reused_p) @ Rz(other_p) @ Ry(reused_p - np.pi / 2) @ Rx(extra_param) @ zero_state
)
) / 2
grad_true1 = (
expZ(
Rx(reused_p + np.pi / 2) @ Rz(other_p) @ Ry(reused_p) @ Rx(extra_param) @ zero_state
)
- expZ(
Rx(reused_p - np.pi / 2) @ Rz(other_p) @ Ry(reused_p) @ Rx(extra_param) @ zero_state
)
) / 2
expected = grad_true0 + grad_true1 # product rule
assert np.allclose(grad_D[0], expected, atol=tol, rtol=0)
def test_gradient_repeated_gate_parameters(self, mocker, dev):
"""Tests that repeated use of a free parameter in a multi-parameter gate yields correct
gradients."""
params = np.array([0.8, 1.3], requires_grad=True)
def circuit(params):
qml.RX(np.array(np.pi / 4, requires_grad=False), wires=[0])
qml.Rot(params[1], params[0], 2 * params[0], wires=[0])
return qml.expval(qml.PauliX(0))
spy_analytic = mocker.spy(dev, "adjoint_jacobian")
h = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
cost = QNode(circuit, dev, diff_method="finite-diff", h=h)
grad_fn = qml.grad(cost)
grad_F = grad_fn(params)
spy_analytic.assert_not_called()
cost = QNode(circuit, dev, diff_method="adjoint")
grad_fn = qml.grad(cost)
grad_D = grad_fn(params)
spy_analytic.assert_called_once()
# the different methods agree
assert np.allclose(grad_D, grad_F, atol=tol, rtol=0)
def test_interface_tf(self, dev):
"""Test if gradients agree between the adjoint and finite-diff methods when using the
TensorFlow interface"""
tf = pytest.importorskip("tensorflow")
def f(params1, params2):
qml.RX(0.4, wires=[0])
qml.RZ(params1 * tf.sqrt(params2), wires=[0])
qml.RY(tf.cos(params2), wires=[0])
return qml.expval(qml.PauliZ(0))
if dev.R_DTYPE == np.float32:
tf_r_dtype = tf.float32
else:
tf_r_dtype = tf.float64
params1 = tf.Variable(0.3, dtype=tf_r_dtype)
params2 = tf.Variable(0.4, dtype=tf_r_dtype)
h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
qnode1 = QNode(f, dev, interface="tf", diff_method="adjoint")
qnode2 = QNode(f, dev, interface="tf", diff_method="finite-diff", h=h)
with tf.GradientTape() as tape:
res1 = qnode1(params1, params2)
g1 = tape.gradient(res1, [params1, params2])
with tf.GradientTape() as tape:
res2 = qnode2(params1, params2)
g2 = tape.gradient(res2, [params1, params2])
assert np.allclose(g1, g2, atol=tol)
def test_interface_torch(self, dev):
"""Test if gradients agree between the adjoint and finite-diff methods when using the
Torch interface"""
torch = pytest.importorskip("torch")
def f(params1, params2):
qml.RX(0.4, wires=[0])
qml.RZ(params1 * torch.sqrt(params2), wires=[0])
qml.RY(torch.cos(params2), wires=[0])
return qml.expval(qml.PauliZ(0))
params1 = torch.tensor(0.3, requires_grad=True)
params2 = torch.tensor(0.4, requires_grad=True)
h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7
qnode1 = QNode(f, dev, interface="torch", diff_method="adjoint")
qnode2 = QNode(f, dev, interface="torch", diff_method="finite-diff", h=h)
res1 = qnode1(params1, params2)
res1.backward()
grad_adjoint = params1.grad, params2.grad
res2 = qnode2(params1, params2)
res2.backward()
grad_fd = params1.grad, params2.grad
assert np.allclose(grad_adjoint, grad_fd)
def test_interface_jax(self, dev):
"""Test if the gradients agree between adjoint and finite-difference methods in the
jax interface"""
jax = pytest.importorskip("jax")
if dev.R_DTYPE == np.float64:
from jax import config # pylint: disable=import-outside-toplevel
config.update("jax_enable_x64", True)
def f(params1, params2):
qml.RX(0.4, wires=[0])
qml.RZ(params1 * jax.numpy.sqrt(params2), wires=[0])
qml.RY(jax.numpy.cos(params2), wires=[0])
return qml.expval(qml.PauliZ(0))
params1 = jax.numpy.array(0.3, dev.R_DTYPE)
params2 = jax.numpy.array(0.4, dev.R_DTYPE)
h = 2e-3 if dev.R_DTYPE == np.float32 else 1e-7
tol = 1e-3 if dev.R_DTYPE == np.float32 else 1e-7
qnode_adjoint = QNode(f, dev, interface="jax", diff_method="adjoint")
qnode_fd = QNode(f, dev, interface="jax", diff_method="finite-diff", h=h)
grad_adjoint = jax.grad(qnode_adjoint)(params1, params2)
grad_fd = jax.grad(qnode_fd)(params1, params2)
assert np.allclose(grad_adjoint, grad_fd, atol=tol)
def circuit_ansatz(params, wires):
"""Circuit ansatz containing all the parametrized gates"""
qml.QubitStateVector(unitary_group.rvs(2**8, random_state=0)[0], wires=wires)
qml.RX(params[0], wires=wires[0])
qml.RY(params[1], wires=wires[1])
qml.adjoint(qml.RX(params[2], wires=wires[2]))
qml.RZ(params[0], wires=wires[3])
qml.CRX(params[3], wires=[wires[3], wires[0]])
qml.PhaseShift(params[4], wires=wires[2])
qml.CRY(params[5], wires=[wires[2], wires[1]])
qml.adjoint(qml.CRZ(params[5], wires=[wires[0], wires[3]]))
qml.adjoint(qml.PhaseShift(params[6], wires=wires[0]))
qml.Rot(params[6], params[7], params[8], wires=wires[0])
qml.adjoint(qml.Rot(params[8], params[8], params[9], wires=wires[1]))
qml.MultiRZ(params[11], wires=[wires[0], wires[1]])
qml.PauliRot(params[12], "XXYZ", wires=[wires[0], wires[1], wires[2], wires[3]])
qml.CPhase(params[12], wires=[wires[3], wires[2]])
qml.IsingXX(params[13], wires=[wires[1], wires[0]])
qml.IsingXY(params[14], wires=[wires[3], wires[2]])
qml.IsingYY(params[14], wires=[wires[3], wires[2]])
qml.IsingZZ(params[14], wires=[wires[2], wires[1]])
qml.U1(params[15], wires=wires[0])
qml.U2(params[16], params[17], wires=wires[0])
qml.U3(params[18], params[19], params[20], wires=wires[1])
qml.adjoint(qml.CRot(params[21], params[22], params[23], wires=[wires[1], wires[2]]))
qml.SingleExcitation(params[24], wires=[wires[2], wires[0]])
qml.DoubleExcitation(params[25], wires=[wires[2], wires[0], wires[1], wires[3]])
qml.SingleExcitationPlus(params[26], wires=[wires[0], wires[2]])
qml.SingleExcitationMinus(params[27], wires=[wires[0], wires[2]])
qml.DoubleExcitationPlus(params[27], wires=[wires[2], wires[0], wires[1], wires[3]])
qml.DoubleExcitationMinus(params[27], wires=[wires[2], wires[0], wires[1], wires[3]])
qml.RX(params[28], wires=wires[0])
qml.RX(params[29], wires=wires[1])
@pytest.mark.parametrize(
"returns",
[
qml.PauliZ(0),
qml.PauliX(2),
qml.PauliZ(0) @ qml.PauliY(3),
qml.Hadamard(2),
qml.Hadamard(3) @ qml.PauliZ(2),
qml.PauliX(0) @ qml.PauliY(3),
qml.PauliY(0) @ qml.PauliY(2) @ qml.PauliY(3),
qml.Hermitian(
np.kron(qml.PauliY.compute_matrix(), qml.PauliZ.compute_matrix()),
wires=[3, 2],
),
qml.Hermitian(np.array([[0, 1], [1, 0]], requires_grad=False), wires=0),
qml.Hermitian(np.array([[0, 1], [1, 0]], requires_grad=False), wires=0) @ qml.PauliZ(2),
],
)
def test_integration(returns):
"""Integration tests that compare to default.qubit for a large circuit containing parametrized
operations"""
dev_def = qml.device("default.qubit", wires=range(8))
dev_lightning = qml.device(device_name, wires=range(8), mpi=True)
def circuit(params):
circuit_ansatz(params, wires=range(8))
return qml.expval(returns), qml.expval(qml.PauliY(1))
n_params = 30
params = np.linspace(0, 10, n_params)
qnode_def = qml.QNode(circuit, dev_def)
qnode_lightning = qml.QNode(circuit, dev_lightning, diff_method="adjoint")
def casted_to_array_def(params):
return np.array(qnode_def(params))
def casted_to_array_lightning(params):
return np.array(qnode_lightning(params))
j_def = qml.jacobian(casted_to_array_def)(params)
j_lightning = qml.jacobian(casted_to_array_lightning)(params)
assert np.allclose(j_def, j_lightning)
custom_wires = ["alice", 3.14, -1, 0, "bob", 1, "unit", "test"]
@pytest.mark.parametrize(
"returns",
[
qml.PauliZ(custom_wires[0]),
qml.PauliX(custom_wires[2]),
qml.PauliZ(custom_wires[0]) @ qml.PauliY(custom_wires[3]),
qml.Hadamard(custom_wires[2]),
qml.Hadamard(custom_wires[3]) @ qml.PauliZ(custom_wires[2]),
# qml.Projector([0, 1], wires=[custom_wires[0], custom_wires[2]]) @ qml.Hadamard(custom_wires[3])
# qml.Projector([0, 0], wires=[custom_wires[2], custom_wires[0]])
qml.PauliX(custom_wires[0]) @ qml.PauliY(custom_wires[3]),
qml.PauliY(custom_wires[0]) @ qml.PauliY(custom_wires[2]) @ qml.PauliY(custom_wires[3]),
qml.Hermitian(np.array([[0, 1], [1, 0]], requires_grad=False), wires=custom_wires[0]),
qml.Hermitian(
np.kron(qml.PauliY.compute_matrix(), qml.PauliZ.compute_matrix()),
wires=[custom_wires[3], custom_wires[2]],
),
qml.Hermitian(np.array([[0, 1], [1, 0]], requires_grad=False), wires=custom_wires[0])
@ qml.PauliZ(custom_wires[2]),
],
)
def test_integration_custom_wires(returns):