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test_device.py
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# Copyright 2018-2024 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.
"""
This module contains unit tests for new device API Lightning classes.
"""
# pylint: disable=too-many-arguments, unused-argument
import numpy as np
import pennylane as qml
import pytest
from conftest import PHI, THETA, VARPHI, LightningDevice
from pennylane.devices import DefaultExecutionConfig, DefaultQubit, ExecutionConfig
from pennylane.devices.default_qubit import adjoint_ops
from pennylane.tape import QuantumScript
from pennylane_lightning.lightning_qubit.lightning_qubit import (
_add_adjoint_transforms,
_supports_adjoint,
accepted_observables,
adjoint_measurements,
adjoint_observables,
decompose,
mid_circuit_measurements,
no_sampling,
stopping_condition,
stopping_condition_shots,
validate_adjoint_trainable_params,
validate_device_wires,
validate_measurements,
validate_observables,
)
if not LightningDevice._new_API:
pytest.skip("Exclusive tests for new device API. Skipping.", allow_module_level=True)
if not LightningDevice._CPP_BINARY_AVAILABLE: # pylint: disable=protected-access
pytest.skip("No binary module found. Skipping.", allow_module_level=True)
@pytest.fixture(params=[np.complex64, np.complex128])
def dev(request):
return LightningDevice(wires=3, c_dtype=request.param)
class TestHelpers:
"""Unit tests for helper functions"""
class DummyOperator(qml.operation.Operation, qml.operation.Observable):
"""Dummy operator"""
num_wires = 1
def test_stopping_condition(self):
"""Test that stopping_condition returns whether or not an operation
is supported by the device."""
valid_op = qml.RX(1.23, 0)
invalid_op = self.DummyOperator(0)
assert stopping_condition(valid_op) is True
assert stopping_condition(invalid_op) is False
def test_accepted_observables(self):
"""Test that accepted_observables returns whether or not an observable
is supported by the device."""
valid_obs = qml.Projector([0], 0)
invalid_obs = self.DummyOperator(0)
assert accepted_observables(valid_obs) is True
assert accepted_observables(invalid_obs) is False
@pytest.mark.parametrize(
"obs, expected",
[
(qml.operation.Tensor(qml.Projector([0], 0), qml.PauliZ(1)), False),
(qml.prod(qml.Projector([0], 0), qml.PauliZ(1)), False),
(qml.s_prod(1.5, qml.Projector([0], 0)), False),
(qml.sum(qml.Projector([0], 0), qml.Hadamard(1)), False),
(qml.sum(qml.prod(qml.Projector([0], 0), qml.Y(1)), qml.PauliX(1)), False),
(qml.operation.Tensor(qml.Y(0), qml.Z(1)), True),
(qml.prod(qml.Y(0), qml.PauliZ(1)), True),
(qml.s_prod(1.5, qml.Y(1)), True),
(qml.sum(qml.Y(1), qml.Hadamard(1)), True),
(qml.X(0), True),
(qml.Hermitian(np.eye(4), [0, 1]), True),
],
)
def test_adjoint_observables(self, obs, expected):
"""Test that adjoint_observables returns the expected boolean result for
a given observable"""
assert adjoint_observables(obs) == expected
def test_add_adjoint_transforms(self):
"""Test that the correct transforms are added to the program by _add_adjoint_transforms"""
expected_program = qml.transforms.core.TransformProgram()
name = "adjoint + lightning.qubit"
expected_program.add_transform(no_sampling, name=name)
expected_program.add_transform(
decompose,
stopping_condition=adjoint_ops,
stopping_condition_shots=stopping_condition_shots,
name=name,
skip_initial_state_prep=False,
)
expected_program.add_transform(validate_observables, accepted_observables, name=name)
expected_program.add_transform(
validate_measurements,
analytic_measurements=adjoint_measurements,
name=name,
)
expected_program.add_transform(qml.transforms.broadcast_expand)
expected_program.add_transform(validate_adjoint_trainable_params)
actual_program = qml.transforms.core.TransformProgram()
_add_adjoint_transforms(actual_program)
assert actual_program == expected_program
@pytest.mark.parametrize(
"circuit, expected",
[
(None, True),
(QuantumScript([], [qml.state()]), False),
(QuantumScript([qml.RX(1.23, 0)], [qml.expval(qml.Z(0))]), True),
(QuantumScript([qml.CRot(1.23, 4.56, 7.89, [0, 1])], [qml.expval(qml.Z(0))]), True),
(QuantumScript([qml.Rot(1.23, 4.56, 7.89, 1)], [qml.var(qml.X(0))]), False),
],
)
def test_supports_adjoint(self, circuit, expected):
"""Test that _supports_adjoint returns the correct boolean value."""
assert _supports_adjoint(circuit) == expected
class TestInitialization:
"""Unit tests for device initialization"""
def test_invalid_num_burnin_error(self):
"""Test that an error is raised when num_burnin is more than number of shots"""
n_shots = 10
num_burnin = 11
with pytest.raises(ValueError, match="Shots should be greater than num_burnin."):
_ = LightningDevice(wires=2, shots=n_shots, mcmc=True, num_burnin=num_burnin)
def test_invalid_kernel_name(self):
"""Test that an error is raised when the kernel_name is not "Local" or "NonZeroRandom"."""
_ = LightningDevice(wires=2, shots=1000, mcmc=True, kernel_name="Local")
_ = LightningDevice(wires=2, shots=1000, mcmc=True, kernel_name="NonZeroRandom")
with pytest.raises(
NotImplementedError, match="only 'Local' and 'NonZeroRandom' kernels are supported"
):
_ = LightningDevice(wires=2, shots=1000, mcmc=True, kernel_name="bleh")
class TestExecution:
"""Unit tests for executing quantum tapes on a device"""
@staticmethod
def calculate_reference(tape):
device = DefaultQubit(max_workers=1)
program, _ = device.preprocess()
tapes, transf_fn = program([tape])
results = device.execute(tapes)
return transf_fn(results)
@staticmethod
def process_and_execute(device, tape):
program, _ = device.preprocess()
tapes, transf_fn = program([tape])
results = device.execute(tapes)
return transf_fn(results)
_default_device_options = {
"c_dtype": np.complex128,
"batch_obs": False,
"mcmc": False,
"kernel_name": None,
"num_burnin": 0,
}
@pytest.mark.parametrize(
"config, expected_config",
[
(
DefaultExecutionConfig,
ExecutionConfig(
grad_on_execution=True,
use_device_gradient=False,
device_options=_default_device_options,
),
),
(
ExecutionConfig(gradient_method="best"),
ExecutionConfig(
gradient_method="adjoint",
grad_on_execution=True,
use_device_gradient=True,
device_options=_default_device_options,
),
),
(
ExecutionConfig(
device_options={
"c_dtype": np.complex64,
"mcmc": True,
}
),
ExecutionConfig(
grad_on_execution=True,
use_device_gradient=False,
device_options={
"c_dtype": np.complex64,
"batch_obs": False,
"mcmc": True,
"kernel_name": None,
"num_burnin": 0,
},
),
),
(
ExecutionConfig(
gradient_method="backprop", use_device_gradient=False, grad_on_execution=False
),
ExecutionConfig(
gradient_method="backprop",
use_device_gradient=False,
grad_on_execution=False,
device_options=_default_device_options,
),
),
],
)
def test_preprocess_correct_config_setup(self, config, expected_config):
"""Test that the execution config is set up correctly in preprocess"""
device = LightningDevice(wires=2)
_, new_config = device.preprocess(config)
del new_config.device_options["rng"]
assert new_config == expected_config
@pytest.mark.parametrize("adjoint", [True, False])
def test_preprocess(self, adjoint):
"""Test that the transform program returned by preprocess is correct"""
device = LightningDevice(wires=2)
expected_program = qml.transforms.core.TransformProgram()
expected_program.add_transform(validate_measurements, name=device.name)
expected_program.add_transform(validate_observables, accepted_observables, name=device.name)
expected_program.add_transform(validate_device_wires, device.wires, name=device.name)
expected_program.add_transform(mid_circuit_measurements, device=device)
expected_program.add_transform(
decompose,
stopping_condition=stopping_condition,
stopping_condition_shots=stopping_condition_shots,
skip_initial_state_prep=True,
name=device.name,
)
expected_program.add_transform(qml.transforms.broadcast_expand)
if adjoint:
name = "adjoint + lightning.qubit"
expected_program.add_transform(no_sampling, name=name)
expected_program.add_transform(
decompose,
stopping_condition=adjoint_ops,
stopping_condition_shots=stopping_condition_shots,
name=name,
skip_initial_state_prep=False,
)
expected_program.add_transform(validate_observables, accepted_observables, name=name)
expected_program.add_transform(
validate_measurements,
analytic_measurements=adjoint_measurements,
name=name,
)
expected_program.add_transform(qml.transforms.broadcast_expand)
expected_program.add_transform(validate_adjoint_trainable_params)
gradient_method = "adjoint" if adjoint else None
config = ExecutionConfig(gradient_method=gradient_method)
actual_program, _ = device.preprocess(config)
assert actual_program == expected_program
@pytest.mark.parametrize(
"op, is_trainable",
[
(qml.StatePrep([1 / np.sqrt(2), 1 / np.sqrt(2)], wires=0), False),
(qml.StatePrep(qml.numpy.array([1 / np.sqrt(2), 1 / np.sqrt(2)]), wires=0), True),
(qml.StatePrep(np.array([1, 0]), wires=0), False),
(qml.BasisState([1, 1], wires=[0, 1]), False),
(qml.BasisState(qml.numpy.array([1, 1]), wires=[0, 1]), True),
],
)
def test_preprocess_state_prep_first_op_decomposition(self, op, is_trainable):
"""Test that state prep ops in the beginning of a tape are decomposed with adjoint
but not otherwise."""
tape = qml.tape.QuantumScript([op, qml.RX(1.23, wires=0)], [qml.expval(qml.PauliZ(0))])
device = LightningDevice(wires=3)
if is_trainable:
# Need to decompose twice as the state prep ops we use first decompose into a template
decomp = op.decomposition()[0].decomposition()
else:
decomp = [op]
config = ExecutionConfig(gradient_method="best" if is_trainable else None)
program, _ = device.preprocess(config)
[new_tape], _ = program([tape])
expected_tape = qml.tape.QuantumScript([*decomp, qml.RX(1.23, wires=0)], tape.measurements)
assert qml.equal(new_tape, expected_tape)
@pytest.mark.parametrize(
"op, decomp_depth",
[
(qml.StatePrep([1 / np.sqrt(2), 1 / np.sqrt(2)], wires=0), 1),
(qml.StatePrep(np.array([1, 0]), wires=0), 1),
(qml.BasisState([1, 1], wires=[0, 1]), 1),
(qml.BasisState(qml.numpy.array([1, 1]), wires=[0, 1]), 1),
(qml.AmplitudeEmbedding([1 / np.sqrt(2), 1 / np.sqrt(2)], wires=0), 2),
(qml.MottonenStatePreparation([1 / np.sqrt(2), 1 / np.sqrt(2)], wires=0), 0),
],
)
def test_preprocess_state_prep_middle_op_decomposition(self, op, decomp_depth):
"""Test that state prep ops in the middle of a tape are always decomposed."""
tape = qml.tape.QuantumScript(
[qml.RX(1.23, wires=0), op, qml.CNOT([0, 1])], [qml.expval(qml.PauliZ(0))]
)
device = LightningDevice(wires=3)
for _ in range(decomp_depth):
op = op.decomposition()[0]
decomp = op.decomposition()
program, _ = device.preprocess()
[new_tape], _ = program([tape])
expected_tape = qml.tape.QuantumScript(
[qml.RX(1.23, wires=0), *decomp, qml.CNOT([0, 1])], tape.measurements
)
assert qml.equal(new_tape, expected_tape)
@pytest.mark.usefixtures("use_legacy_and_new_opmath")
@pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
@pytest.mark.parametrize(
"mp",
[
qml.probs(wires=[1, 2]),
qml.probs(op=qml.Z(2)),
qml.expval(qml.Z(2)),
qml.var(qml.X(2)),
qml.expval(qml.X(0) + qml.Z(0)),
qml.expval(qml.Hamiltonian([-0.5, 1.5], [qml.Y(1), qml.X(1)])),
qml.expval(2.5 * qml.Z(0)),
qml.expval(qml.Z(0) @ qml.X(1)),
qml.expval(qml.operation.Tensor(qml.Z(0), qml.X(1))),
qml.expval(
qml.SparseHamiltonian(
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]).sparse_matrix(
wire_order=[0, 1, 2]
),
wires=[0, 1, 2],
)
),
qml.expval(qml.Projector([1], wires=2)),
],
)
def test_execute_single_measurement(self, theta, phi, mp, dev):
"""Test that execute returns the correct results with a single measurement."""
if isinstance(mp.obs, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
mp.obs = qml.operation.convert_to_legacy_H(mp.obs)
qs = QuantumScript(
[
qml.RX(phi, 0),
qml.CNOT([0, 2]),
qml.RZ(theta, 1),
qml.CNOT([1, 2]),
],
[mp],
)
res = self.process_and_execute(dev, qs)[0]
expected = self.calculate_reference(qs)[0]
assert np.allclose(res, expected)
@pytest.mark.usefixtures("use_legacy_and_new_opmath")
@pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
@pytest.mark.parametrize(
"mp1",
[
qml.probs(wires=[1, 2]),
qml.expval(qml.Z(2)),
qml.var(qml.X(2)),
qml.var(qml.Hermitian(qml.Hadamard.compute_matrix(), 0)),
],
)
@pytest.mark.parametrize(
"mp2",
[
qml.probs(op=qml.X(2)),
qml.expval(qml.Y(2)),
qml.var(qml.Y(2)),
qml.expval(qml.Hamiltonian([-0.5, 1.5, -1.1], [qml.Y(1), qml.X(1), qml.Z(0)])),
],
)
def test_execute_multi_measurement(self, theta, phi, dev, mp1, mp2):
"""Test that execute returns the correct results with multiple measurements."""
if isinstance(mp2.obs, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
mp2.obs = qml.operation.convert_to_legacy_H(mp2.obs)
qs = QuantumScript(
[
qml.RX(phi, 0),
qml.CNOT([0, 2]),
qml.RZ(theta, 1),
qml.CNOT([1, 2]),
],
[mp1, mp2],
)
res = self.process_and_execute(dev, qs)[0]
expected = self.calculate_reference(qs)[0]
assert len(res) == 2
for r, e in zip(res, expected):
assert np.allclose(r, e)
@pytest.mark.parametrize("phi, theta", list(zip(PHI, THETA)))
@pytest.mark.parametrize("wires", (["a", "b", -3], [0, "target", "other_target"]))
def test_custom_wires(self, phi, theta, wires):
"""Test execution with custom wires"""
device = LightningDevice(wires=wires)
qs = QuantumScript(
[
qml.RX(phi, wires[0]),
qml.RY(theta, wires[2]),
qml.CNOT([wires[0], wires[1]]),
qml.CNOT([wires[1], wires[2]]),
],
[qml.expval(qml.Z(wires[0])), qml.expval(qml.Z(wires[2]))],
)
result = device.execute(qs)
assert isinstance(result, tuple)
assert len(result) == 2
assert np.allclose(result[0], np.cos(phi))
assert np.allclose(result[1], np.cos(phi) * np.cos(theta))
@pytest.mark.parametrize(
"wires, wire_order", [(3, (0, 1, 2)), (("a", "b", "c"), ("a", "b", "c"))]
)
def test_probs_different_wire_orders(self, wires, wire_order):
"""Test that measuring probabilities works with custom wires."""
dev = LightningDevice(wires=wires)
op = qml.Hadamard(wire_order[1])
tape = QuantumScript([op], [qml.probs(wires=(wire_order[0], wire_order[1]))])
res = dev.execute(tape)
assert qml.math.allclose(res, np.array([0.5, 0.5, 0.0, 0.0]))
tape2 = QuantumScript([op], [qml.probs(wires=(wire_order[1], wire_order[2]))])
res2 = dev.execute(tape2)
assert qml.math.allclose(res2, np.array([0.5, 0.0, 0.5, 0.0]))
tape3 = QuantumScript([op], [qml.probs(wires=(wire_order[1], wire_order[0]))])
res3 = dev.execute(tape3)
assert qml.math.allclose(res3, np.array([0.5, 0.0, 0.5, 0.0]))
@pytest.mark.parametrize("batch_obs", [True, False])
class TestDerivatives:
"""Unit tests for calculating derivatives with a device"""
@staticmethod
def calculate_reference(tape, execute_and_derivatives=False):
device = DefaultQubit(max_workers=1)
program, config = device.preprocess(ExecutionConfig(gradient_method="adjoint"))
tapes, transf_fn = program([tape])
if execute_and_derivatives:
results, jac = device.execute_and_compute_derivatives(tapes, config)
else:
results = device.execute(tapes, config)
jac = device.compute_derivatives(tapes, config)
return transf_fn(results), jac
@staticmethod
def process_and_execute(device, tape, execute_and_derivatives=False, obs_batch=False):
program, config = device.preprocess(
ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": obs_batch})
)
tapes, transf_fn = program([tape])
if execute_and_derivatives:
results, jac = device.execute_and_compute_derivatives(tapes, config)
else:
results = device.execute(tapes, config)
jac = device.compute_derivatives(tapes, config)
return transf_fn(results), jac
@pytest.mark.parametrize(
"config, tape, expected",
[
(None, None, True),
(DefaultExecutionConfig, None, False),
(ExecutionConfig(gradient_method="backprop"), None, False),
(
ExecutionConfig(gradient_method="backprop"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))]),
False,
),
(ExecutionConfig(gradient_method="best"), None, True),
(ExecutionConfig(gradient_method="adjoint"), None, True),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))]),
True,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.var(qml.Z(0))]),
False,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.state()]),
False,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))], shots=10),
False,
),
],
)
def test_supports_derivatives(self, dev, config, tape, expected, batch_obs):
"""Test that supports_derivative returns the correct boolean value."""
assert dev.supports_derivatives(config, tape) == expected
@pytest.mark.usefixtures("use_legacy_and_new_opmath")
@pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
@pytest.mark.parametrize(
"obs",
[
qml.Z(1),
2.5 * qml.Z(0),
qml.Z(0) @ qml.X(1),
qml.Z(1) + qml.X(1),
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]),
qml.Hermitian(qml.Hadamard.compute_matrix(), 0),
qml.Projector([1], 1),
],
)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_derivatives_single_expval(
self, theta, phi, dev, obs, execute_and_derivatives, batch_obs
):
"""Test that the jacobian is correct when a tape has a single expectation value"""
if isinstance(obs, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs = qml.operation.convert_to_legacy_H(obs)
qs = QuantumScript(
[qml.RX(theta, 0), qml.CNOT([0, 1]), qml.RY(phi, 1)],
[qml.expval(obs)],
trainable_params=[0, 1],
)
res, jac = self.process_and_execute(
dev, qs, execute_and_derivatives=execute_and_derivatives, obs_batch=batch_obs
)
if isinstance(obs, qml.Hamiltonian):
qs = QuantumScript(
qs.operations,
[qml.expval(qml.Hermitian(qml.matrix(obs), wires=obs.wires))],
trainable_params=qs.trainable_params,
)
expected, expected_jac = self.calculate_reference(
qs, execute_and_derivatives=execute_and_derivatives
)
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert len(res) == len(jac) == 1
assert np.allclose(res, expected, atol=tol, rtol=0)
assert np.allclose(jac, expected_jac, atol=tol, rtol=0)
@pytest.mark.parametrize("theta, phi, omega", list(zip(THETA, PHI, VARPHI)))
@pytest.mark.parametrize(
"obs1",
[
qml.Z(1),
2.5 * qml.Y(2),
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]),
qml.Z(0) @ qml.X(1),
],
)
@pytest.mark.parametrize(
"obs2",
[
qml.Y(0) @ qml.X(2),
qml.Z(1) + qml.X(1),
qml.SparseHamiltonian(
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]).sparse_matrix(
wire_order=[0, 1, 2]
),
wires=[0, 1, 2],
),
qml.Projector([1], wires=2),
],
)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_derivatives_multi_expval(
self, theta, phi, omega, dev, obs1, obs2, execute_and_derivatives, batch_obs
):
"""Test that the jacobian is correct when a tape has multiple expectation values"""
if isinstance(obs1, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs1 = qml.operation.convert_to_legacy_H(obs1)
if isinstance(obs2, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs2 = qml.operation.convert_to_legacy_H(obs2)
qs = QuantumScript(
[
qml.RX(theta, 0),
qml.CNOT([0, 1]),
qml.RY(phi, 1),
qml.CNOT([1, 2]),
qml.RZ(omega, 2),
],
[qml.expval(obs1), qml.expval(obs2)],
trainable_params=[0, 1, 2],
)
res, jac = self.process_and_execute(
dev, qs, execute_and_derivatives=execute_and_derivatives, obs_batch=batch_obs
)
if isinstance(obs1, qml.Hamiltonian):
qs = QuantumScript(
qs.operations,
[qml.expval(qml.Hermitian(qml.matrix(obs1), wires=obs1.wires)), qml.expval(obs2)],
trainable_params=qs.trainable_params,
)
expected, expected_jac = self.calculate_reference(
qs, execute_and_derivatives=execute_and_derivatives
)
res = res[0]
jac = jac[0]
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert len(res) == len(jac) == 2
assert np.allclose(res, expected, atol=tol, rtol=0)
assert np.allclose(jac, expected_jac, atol=tol, rtol=0)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_derivatives_no_trainable_params(self, dev, execute_and_derivatives, batch_obs):
"""Test that the derivatives are empty with there are no trainable parameters."""
qs = QuantumScript(
[qml.Hadamard(0), qml.CNOT([0, 1]), qml.S(1), qml.T(1)], [qml.expval(qml.Z(1))]
)
res, jac = self.process_and_execute(
dev, qs, execute_and_derivatives=execute_and_derivatives, obs_batch=batch_obs
)
expected, _ = self.calculate_reference(qs, execute_and_derivatives=execute_and_derivatives)
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert np.allclose(res, expected, atol=tol, rtol=0)
assert len(jac) == 1
assert qml.math.shape(jac[0]) == (0,)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
@pytest.mark.parametrize(
"state_prep, params, wires",
[
(qml.BasisState, [1, 1], [0, 1]),
(qml.StatePrep, [0.0, 0.0, 0.0, 1.0], [0, 1]),
(qml.StatePrep, qml.numpy.array([0.0, 1.0]), [1]),
],
)
@pytest.mark.parametrize(
"trainable_params",
[(0, 1, 2), (1, 2)],
)
def test_state_prep_ops(
self, dev, state_prep, params, wires, execute_and_derivatives, batch_obs, trainable_params
):
"""Test that a circuit containing state prep operations is differentiated correctly."""
qs = QuantumScript(
[state_prep(params, wires), qml.RX(1.23, 0), qml.CNOT([0, 1]), qml.RX(4.56, 1)],
[qml.expval(qml.PauliZ(1))],
)
config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs})
program, new_config = dev.preprocess(config)
tapes, fn = program([qs])
tapes[0].trainable_params = trainable_params
if execute_and_derivatives:
res, jac = dev.execute_and_compute_derivatives(tapes, new_config)
res = fn(res)
else:
res, jac = (
fn(dev.execute(tapes, new_config)),
dev.compute_derivatives(tapes, new_config),
)
dev_ref = DefaultQubit(max_workers=1)
config = ExecutionConfig(gradient_method="adjoint")
program, new_config = dev_ref.preprocess(config)
tapes, fn = program([qs])
tapes[0].trainable_params = trainable_params
if execute_and_derivatives:
expected, expected_jac = dev_ref.execute_and_compute_derivatives(tapes, new_config)
expected = fn(expected)
else:
expected, expected_jac = (
fn(dev_ref.execute(tapes, new_config)),
dev_ref.compute_derivatives(tapes, new_config),
)
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert np.allclose(res, expected, atol=tol, rtol=0)
assert np.allclose(jac, expected_jac, atol=tol, rtol=0)
def test_state_jacobian_not_supported(self, dev, batch_obs):
"""Test that an error is raised if derivatives are requested for state measurement"""
qs = QuantumScript([qml.RX(1.23, 0)], [qml.state()], trainable_params=[0])
config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs})
with pytest.raises(
qml.QuantumFunctionError, match="This method does not support statevector return type"
):
_ = dev.compute_derivatives(qs, config)
with pytest.raises(
qml.QuantumFunctionError, match="This method does not support statevector return type"
):
_ = dev.execute_and_compute_derivatives(qs, config)
def test_shots_error_with_derivatives(self, dev, batch_obs):
"""Test that an error is raised if the gradient method is adjoint when the tape has shots"""
qs = QuantumScript(
[qml.RX(1.23, 0)], [qml.expval(qml.Z(0))], shots=10, trainable_params=[0]
)
config = ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": batch_obs})
program, _ = dev.preprocess(config)
with pytest.raises(qml.DeviceError, match="Finite shots are not supported"):
_, _ = program([qs])
@pytest.mark.parametrize("phi", PHI)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_derivatives_tape_batch(self, phi, execute_and_derivatives, batch_obs):
"""Test that results are correct when we execute and compute derivatives for a batch of
tapes."""
device = LightningDevice(wires=4, batch_obs=batch_obs)
ops = [
qml.X(0),
qml.X(1),
qml.ctrl(qml.RX(phi, 2), (0, 1, 3), control_values=[1, 1, 0]),
]
qs1 = QuantumScript(
ops,
[
qml.expval(qml.sum(qml.Y(2), qml.Z(1))),
qml.expval(qml.s_prod(3, qml.Z(2))),
],
trainable_params=[0],
)
ops = [qml.Hadamard(0), qml.IsingXX(phi, wires=(0, 1))]
qs2 = QuantumScript(ops, [qml.expval(qml.prod(qml.Z(0), qml.Z(1)))], trainable_params=[0])
if execute_and_derivatives:
results, jacs = device.execute_and_compute_derivatives((qs1, qs2))
else:
results = device.execute((qs1, qs2))
jacs = device.compute_derivatives((qs1, qs2))
# Assert results
expected1 = (-np.sin(phi) - 1, 3 * np.cos(phi))
x1 = np.cos(phi / 2) ** 2 / 2
x2 = np.sin(phi / 2) ** 2 / 2
expected2 = sum([x1, -x2, -x1, x2]) # zero
expected = (expected1, expected2)
assert len(results) == len(expected)
assert len(results[0]) == len(expected[0])
assert np.allclose(results[0][0], expected[0][0])
assert np.allclose(results[0][1], expected[0][1])
assert np.allclose(results[1], expected[1])
# Assert derivatives
expected_jac1 = (-np.cos(phi), -3 * np.sin(phi))
x1_jac = -np.cos(phi / 2) * np.sin(phi / 2) / 2
x2_jac = np.sin(phi / 2) * np.cos(phi / 2) / 2
expected_jac2 = sum([x1_jac, -x2_jac, -x1_jac, x2_jac]) # zero
expected_jac = (expected_jac1, expected_jac2)
assert len(jacs) == len(expected_jac)
assert len(jacs[0]) == len(expected_jac[0])
assert np.allclose(jacs[0], expected_jac[0])
assert np.allclose(jacs[1], expected_jac[1])
@pytest.mark.parametrize("batch_obs", [True, False])
class TestVJP:
"""Unit tests for VJP computation with the new device API."""
@staticmethod
def calculate_reference(tape, dy, execute_and_derivatives=False):
device = DefaultQubit(max_workers=1)
program, config = device.preprocess(ExecutionConfig(gradient_method="adjoint"))
tapes, transf_fn = program([tape])
dy = [dy]
if execute_and_derivatives:
results, jac = device.execute_and_compute_vjp(tapes, dy, config)
else:
results = device.execute(tapes, config)
jac = device.compute_vjp(tapes, dy, config)
return transf_fn(results), jac
@staticmethod
def process_and_execute(device, tape, dy, execute_and_derivatives=False, obs_batch=False):
program, config = device.preprocess(
ExecutionConfig(gradient_method="adjoint", device_options={"batch_obs": obs_batch})
)
tapes, transf_fn = program([tape])
dy = [dy]
if execute_and_derivatives:
results, jac = device.execute_and_compute_vjp(tapes, dy, config)
else:
results = device.execute(tapes, config)
jac = device.compute_vjp(tapes, dy, config)
return transf_fn(results), jac
@pytest.mark.parametrize(
"config, tape, expected",
[
(None, None, True),
(DefaultExecutionConfig, None, False),
(ExecutionConfig(gradient_method="backprop"), None, False),
(
ExecutionConfig(gradient_method="backprop"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))]),
False,
),
(ExecutionConfig(gradient_method="best"), None, True),
(ExecutionConfig(gradient_method="adjoint"), None, True),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))]),
True,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.var(qml.Z(0))]),
False,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.state()]),
False,
),
(
ExecutionConfig(gradient_method="adjoint"),
QuantumScript([qml.RX(0.123, 0)], [qml.expval(qml.Z(0))], shots=10),
False,
),
],
)
def test_supports_vjp(self, dev, config, tape, expected, batch_obs):
"""Test that supports_vjp returns the correct boolean value."""
assert dev.supports_vjp(config, tape) == expected
@pytest.mark.usefixtures("use_legacy_and_new_opmath")
@pytest.mark.parametrize("theta, phi", list(zip(THETA, PHI)))
@pytest.mark.parametrize(
"obs",
[
qml.Z(1),
2.5 * qml.Z(0),
qml.Z(0) @ qml.X(1),
qml.Z(1) + qml.X(1),
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]),
qml.Hermitian(qml.Hadamard.compute_matrix(), 0),
qml.Projector([1], 1),
],
)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_vjp_single_expval(self, theta, phi, dev, obs, execute_and_derivatives, batch_obs):
"""Test that the VJP is correct when a tape has a single expectation value"""
if isinstance(obs, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs = qml.operation.convert_to_legacy_H(obs)
qs = QuantumScript(
[qml.RX(theta, 0), qml.CNOT([0, 1]), qml.RY(phi, 1)],
[qml.expval(obs)],
trainable_params=[0, 1],
)
dy = 1.0
res, jac = self.process_and_execute(
dev, qs, dy, execute_and_derivatives=execute_and_derivatives, obs_batch=batch_obs
)
if isinstance(obs, qml.Hamiltonian):
qs = QuantumScript(
qs.operations,
[qml.expval(qml.Hermitian(qml.matrix(obs), wires=obs.wires))],
trainable_params=qs.trainable_params,
)
expected, expected_jac = self.calculate_reference(
qs, dy, execute_and_derivatives=execute_and_derivatives
)
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert len(res) == len(jac) == 1
assert np.allclose(res, expected, atol=tol, rtol=0)
assert np.allclose(jac, expected_jac, atol=tol, rtol=0)
@pytest.mark.parametrize("theta, phi, omega", list(zip(THETA, PHI, VARPHI)))
@pytest.mark.parametrize(
"obs1",
[
qml.Z(1),
2.5 * qml.Y(2),
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]),
qml.Z(0) @ qml.X(1),
],
)
@pytest.mark.parametrize(
"obs2",
[
qml.Y(0) @ qml.X(2),
qml.Z(1) + qml.X(1),
qml.SparseHamiltonian(
qml.Hamiltonian([-1.0, 1.5], [qml.Z(1), qml.X(1)]).sparse_matrix(
wire_order=[0, 1, 2]
),
wires=[0, 1, 2],
),
qml.Projector([1], wires=2),
],
)
@pytest.mark.parametrize("execute_and_derivatives", [True, False])
def test_vjp_multi_expval(
self, theta, phi, omega, dev, obs1, obs2, execute_and_derivatives, batch_obs
):
"""Test that the VJP is correct when a tape has multiple expectation values"""
if isinstance(obs1, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs1 = qml.operation.convert_to_legacy_H(obs1)
if isinstance(obs2, qml.ops.LinearCombination) and not qml.operation.active_new_opmath():
obs2 = qml.operation.convert_to_legacy_H(obs2)
qs = QuantumScript(
[
qml.RX(theta, 0),
qml.CNOT([0, 1]),
qml.RY(phi, 1),
qml.CNOT([1, 2]),
qml.RZ(omega, 2),
],
[qml.expval(obs1), qml.expval(obs2)],
trainable_params=[0, 1, 2],
)
dy = (1.0, 2.0)
res, jac = self.process_and_execute(
dev, qs, dy, execute_and_derivatives=execute_and_derivatives, obs_batch=batch_obs
)
if isinstance(obs1, qml.Hamiltonian):
qs = QuantumScript(
qs.operations,
[qml.expval(qml.Hermitian(qml.matrix(obs1), wires=obs1.wires)), qml.expval(obs2)],
trainable_params=qs.trainable_params,
)
expected, expected_jac = self.calculate_reference(
qs, dy, execute_and_derivatives=execute_and_derivatives
)
res = res[0]
jac = jac[0]
tol = 1e-5 if dev.c_dtype == np.complex64 else 1e-7
assert len(res) == 2
assert len(jac) == 3
assert np.allclose(res, expected, atol=tol, rtol=0)
assert np.allclose(jac, expected_jac, atol=tol, rtol=0)