def test_uccsd_hf_aer_qasm(self):
""" uccsd hf test with Aer qasm_simulator. """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
optimizer = SPSA(maxiter=200, last_avg=5)
algo = VQE(self.qubit_op,
self.var_form,
optimizer,
expectation=PauliExpectation())
result = algo.run(
QuantumInstance(backend,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
result = self.core.process_algorithm_result(result)
self.assertAlmostEqual(result.energy, -1.138, places=2)
def convert(
self,
operator: OperatorBase,
params: Optional[Union[ParameterVector, ParameterExpression,
List[ParameterExpression]]] = None
) -> OperatorBase:
r"""
Args:
operator: The operator we are taking the gradient of.
params: params: The parameters we are taking the gradient with respect to.
Returns:
An operator whose evaluation yields the Gradient.
Raises:
ValueError: If ``params`` contains a parameter not present in ``operator``.
"""
if params is None:
raise ValueError("No parameters were provided to differentiate")
if isinstance(params, (ParameterVector, list)):
param_grads = [self.convert(operator, param) for param in params]
absent_params = [
params[i] for i, grad_ops in enumerate(param_grads)
if grad_ops is None
]
if len(absent_params) > 0:
raise ValueError(
"The following parameters do not appear in the provided operator: ",
absent_params)
return ListOp(param_grads)
param = params
# Preprocessing
expec_op = PauliExpectation(
group_paulis=False).convert(operator).reduce()
cleaned_op = self._factor_coeffs_out_of_composed_op(expec_op)
return self.get_gradient(cleaned_op, param)
def test_construction(self):
"""Test the correct operator expression is constructed."""
alpha = 0.5
base_expecation = PauliExpectation()
cvar_expecation = CVaRExpectation(alpha=alpha,
expectation=base_expecation)
with self.subTest('single operator'):
op = ~StateFn(Z) @ Plus
expected = CVaRMeasurement(Z, alpha) @ Plus
cvar = cvar_expecation.convert(op)
self.assertEqual(cvar, expected)
with self.subTest('list operator'):
op = ~StateFn(ListOp([Z ^ Z, I ^ Z])) @ (Plus ^ Plus)
expected = ListOp([
CVaRMeasurement((Z ^ Z), alpha) @ (Plus ^ Plus),
CVaRMeasurement((I ^ Z), alpha) @ (Plus ^ Plus)
])
cvar = cvar_expecation.convert(op)
self.assertEqual(cvar, expected)
def test_uccsd_hf_aer_qasm(self):
""" uccsd hf test with Aer qasm_simulator. """
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
optimizer = SPSA(maxiter=200, last_avg=5)
solver = VQE(var_form=self.var_form,
optimizer=optimizer,
expectation=PauliExpectation(),
quantum_instance=QuantumInstance(
backend=backend,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
gsc = GroundStateEigensolver(self.fermionic_transformation, solver)
result = gsc.solve(self.driver)
self.assertAlmostEqual(result.total_energies[0], -1.138, places=2)
def setUp(self) -> None:
super().setUp()
backend = BasicAer.get_backend('qasm_simulator')
self.sampler = CircuitSampler(backend, attach_results=True)
self.expect = PauliExpectation()
class TestPauliExpectation(QiskitAquaTestCase):
"""Pauli Change of Basis Expectation tests."""
def setUp(self) -> None:
super().setUp()
backend = BasicAer.get_backend('qasm_simulator')
self.sampler = CircuitSampler(backend, attach_results=True)
self.expect = PauliExpectation()
def test_pauli_expect_pair(self):
""" pauli expect pair test """
op = (Z ^ Z)
# wf = (Pl^Pl) + (Ze^Ze)
wf = CX @ (H ^ I) @ Zero
converted_meas = self.expect.convert(~StateFn(op) @ wf)
self.assertAlmostEqual(converted_meas.eval(), 0, delta=.1)
sampled = self.sampler.convert(converted_meas)
self.assertAlmostEqual(sampled.eval(), 0, delta=.1)
def test_pauli_expect_single(self):
""" pauli expect single test """
paulis = [Z, X, Y, I]
states = [Zero, One, Plus, Minus, S @ Plus, S @ Minus]
for pauli, state in itertools.product(paulis, states):
converted_meas = self.expect.convert(~StateFn(pauli) @ state)
matmulmean = state.adjoint().to_matrix() @ pauli.to_matrix() @ state.to_matrix()
self.assertAlmostEqual(converted_meas.eval(), matmulmean, delta=.1)
sampled = self.sampler.convert(converted_meas)
self.assertAlmostEqual(sampled.eval(), matmulmean, delta=.1)
def test_pauli_expect_op_vector(self):
""" pauli expect op vector test """
paulis_op = ListOp([X, Y, Z, I])
converted_meas = self.expect.convert(~StateFn(paulis_op))
plus_mean = (converted_meas @ Plus)
np.testing.assert_array_almost_equal(plus_mean.eval(), [1, 0, 0, 1], decimal=1)
sampled_plus = self.sampler.convert(plus_mean)
np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, 0, 0, 1], decimal=1)
minus_mean = (converted_meas @ Minus)
np.testing.assert_array_almost_equal(minus_mean.eval(), [-1, 0, 0, 1], decimal=1)
sampled_minus = self.sampler.convert(minus_mean)
np.testing.assert_array_almost_equal(sampled_minus.eval(), [-1, 0, 0, 1], decimal=1)
zero_mean = (converted_meas @ Zero)
np.testing.assert_array_almost_equal(zero_mean.eval(), [0, 0, 1, 1], decimal=1)
sampled_zero = self.sampler.convert(zero_mean)
np.testing.assert_array_almost_equal(sampled_zero.eval(), [0, 0, 1, 1], decimal=1)
sum_zero = (Plus + Minus) * (.5 ** .5)
sum_zero_mean = (converted_meas @ sum_zero)
np.testing.assert_array_almost_equal(sum_zero_mean.eval(), [0, 0, 1, 1], decimal=1)
sampled_zero_mean = self.sampler.convert(sum_zero_mean)
# !!NOTE!!: Depolarizing channel (Sampling) means interference
# does not happen between circuits in sum, so expectation does
# not equal expectation for Zero!!
np.testing.assert_array_almost_equal(sampled_zero_mean.eval(), [0, 0, 0, 1], decimal=1)
for i, op in enumerate(paulis_op.oplist):
mat_op = op.to_matrix()
np.testing.assert_array_almost_equal(zero_mean.eval()[i],
Zero.adjoint().to_matrix() @
mat_op @ Zero.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(plus_mean.eval()[i],
Plus.adjoint().to_matrix() @
mat_op @ Plus.to_matrix(),
decimal=1)
np.testing.assert_array_almost_equal(minus_mean.eval()[i],
Minus.adjoint().to_matrix() @
mat_op @ Minus.to_matrix(),
decimal=1)
def test_pauli_expect_state_vector(self):
""" pauli expect state vector test """
states_op = ListOp([One, Zero, Plus, Minus])
paulis_op = X
converted_meas = self.expect.convert(~StateFn(paulis_op) @ states_op)
np.testing.assert_array_almost_equal(converted_meas.eval(), [0, 0, 1, -1], decimal=1)
sampled = self.sampler.convert(converted_meas)
np.testing.assert_array_almost_equal(sampled.eval(), [0, 0, 1, -1], decimal=1)
# Small test to see if execution results are accessible
for composed_op in sampled:
self.assertIn('counts', composed_op[1].execution_results)
def test_pauli_expect_op_vector_state_vector(self):
""" pauli expect op vector state vector test """
paulis_op = ListOp([X, Y, Z, I])
states_op = ListOp([One, Zero, Plus, Minus])
valids = [[+0, 0, 1, -1],
[+0, 0, 0, 0],
[-1, 1, 0, -0],
[+1, 1, 1, 1]]
converted_meas = self.expect.convert(~StateFn(paulis_op) @ states_op)
np.testing.assert_array_almost_equal(converted_meas.eval(), valids, decimal=1)
sampled = self.sampler.convert(converted_meas)
np.testing.assert_array_almost_equal(sampled.eval(), valids, decimal=1)
def test_not_to_matrix_called(self):
""" 45 qubit calculation - literally will not work if to_matrix is
somehow called (in addition to massive=False throwing an error)"""
qs = 45
states_op = ListOp([Zero ^ qs,
One ^ qs,
(Zero ^ qs) + (One ^ qs)])
paulis_op = ListOp([Z ^ qs,
(I ^ Z ^ I) ^ int(qs / 3)])
converted_meas = self.expect.convert(~StateFn(paulis_op) @ states_op)
np.testing.assert_array_almost_equal(converted_meas.eval(), [[1, -1, 0], [1, -1, 0]])
def test_grouped_pauli_expectation(self):
""" grouped pauli expectation test """
two_qubit_H2 = (-1.052373245772859 * I ^ I) + \
(0.39793742484318045 * I ^ Z) + \
(-0.39793742484318045 * Z ^ I) + \
(-0.01128010425623538 * Z ^ Z) + \
(0.18093119978423156 * X ^ X)
wf = CX @ (H ^ I) @ Zero
expect_op = PauliExpectation(group_paulis=False).convert(~StateFn(two_qubit_H2) @ wf)
self.sampler._extract_circuitstatefns(expect_op)
num_circuits_ungrouped = len(self.sampler._circuit_ops_cache)
self.assertEqual(num_circuits_ungrouped, 5)
expect_op_grouped = PauliExpectation(group_paulis=True).convert(~StateFn(two_qubit_H2) @ wf)
sampler = CircuitSampler(BasicAer.get_backend('statevector_simulator'))
sampler._extract_circuitstatefns(expect_op_grouped)
num_circuits_grouped = len(sampler._circuit_ops_cache)
self.assertEqual(num_circuits_grouped, 2)
@unittest.skip(reason="IBMQ testing not available in general.")
def test_ibmq_grouped_pauli_expectation(self):
""" pauli expect op vector state vector test """
from qiskit import IBMQ
p = IBMQ.load_account()
backend = p.get_backend('ibmq_qasm_simulator')
paulis_op = ListOp([X, Y, Z, I])
states_op = ListOp([One, Zero, Plus, Minus])
valids = [[+0, 0, 1, -1],
[+0, 0, 0, 0],
[-1, 1, 0, -0],
[+1, 1, 1, 1]]
converted_meas = self.expect.convert(~StateFn(paulis_op) @ states_op)
sampled = CircuitSampler(backend).convert(converted_meas)
np.testing.assert_array_almost_equal(sampled.eval(), valids, decimal=1)
def test_multi_representation_ops(self):
""" Test observables with mixed representations """
mixed_ops = ListOp([X.to_matrix_op(),
H,
H + I,
X])
converted_meas = self.expect.convert(~StateFn(mixed_ops))
plus_mean = (converted_meas @ Plus)
sampled_plus = self.sampler.convert(plus_mean)
np.testing.assert_array_almost_equal(sampled_plus.eval(),
[1, .5**.5, (1 + .5**.5), 1],
decimal=1)
def test_h2_bopes_sampler(self):
"""Test BOPES Sampler on H2"""
seed = 50
aqua_globals.random_seed = seed
# Molecule
dof = partial(Molecule.absolute_distance, atom_pair=(1, 0))
m = Molecule(geometry=[['H', [0., 0., 1.]], ['H', [0., 0.45, 1.]]],
degrees_of_freedom=[dof])
f_t = FermionicTransformation()
driver = PySCFDriver(molecule=m)
qubitop, _ = f_t.transform(driver)
# Quantum Instance:
shots = 1
backend = 'statevector_simulator'
quantum_instance = QuantumInstance(BasicAer.get_backend(backend),
shots=shots)
quantum_instance.run_config.seed_simulator = seed
quantum_instance.compile_config['seed_transpiler'] = seed
# Variational form
i_state = HartreeFock(
num_orbitals=f_t._molecule_info['num_orbitals'],
qubit_mapping=f_t._qubit_mapping,
two_qubit_reduction=f_t._two_qubit_reduction,
num_particles=f_t._molecule_info['num_particles'],
sq_list=f_t._molecule_info['z2_symmetries'].sq_list)
var_form = RealAmplitudes(qubitop.num_qubits,
reps=1,
entanglement='full',
skip_unentangled_qubits=False)
var_form.compose(i_state, front=True)
# Classical optimizer:
# Analytic Quantum Gradient Descent (AQGD) (with Epochs)
aqgd_max_iter = [10] + [1] * 100
aqgd_eta = [1e0] + [1.0 / k for k in range(1, 101)]
aqgd_momentum = [0.5] + [0.5] * 100
optimizer = AQGD(maxiter=aqgd_max_iter,
eta=aqgd_eta,
momentum=aqgd_momentum,
tol=1e-6,
averaging=4)
# Min Eigensolver: VQE
solver = VQE(var_form=var_form,
optimizer=optimizer,
quantum_instance=quantum_instance,
expectation=PauliExpectation())
me_gss = GroundStateEigensolver(f_t, solver)
# BOPES sampler
sampler = BOPESSampler(gss=me_gss)
# absolute internuclear distance in Angstrom
points = [0.7, 1.0, 1.3]
results = sampler.sample(driver, points)
points_run = results.points
energies = results.energies
np.testing.assert_array_almost_equal(points_run, [0.7, 1.0, 1.3])
np.testing.assert_array_almost_equal(
energies, [-1.13618945, -1.10115033, -1.03518627], decimal=2)
class TestVQE(QiskitAquaTestCase):
""" Test VQE """
def setUp(self):
super().setUp()
self.seed = 50
aqua_globals.random_seed = self.seed
self.h2_op = -1.052373245772859 * (I ^ I) \
+ 0.39793742484318045 * (I ^ Z) \
- 0.39793742484318045 * (Z ^ I) \
- 0.01128010425623538 * (Z ^ Z) \
+ 0.18093119978423156 * (X ^ X)
self.h2_energy = -1.85727503
self.ryrz_wavefunction = TwoLocal(rotation_blocks=['ry', 'rz'],
entanglement_blocks='cz')
self.ry_wavefunction = TwoLocal(rotation_blocks='ry',
entanglement_blocks='cz')
self.qasm_simulator = QuantumInstance(
BasicAer.get_backend('qasm_simulator'),
shots=1024,
seed_simulator=self.seed,
seed_transpiler=self.seed)
self.statevector_simulator = QuantumInstance(
BasicAer.get_backend('statevector_simulator'),
shots=1,
seed_simulator=self.seed,
seed_transpiler=self.seed)
def test_basic_aer_statevector(self):
"""Test the VQE on BasicAer's statevector simulator."""
wavefunction = self.ryrz_wavefunction
vqe = VQE(self.h2_op, wavefunction, L_BFGS_B())
result = vqe.run(
QuantumInstance(BasicAer.get_backend('statevector_simulator'),
basis_gates=['u1', 'u2', 'u3', 'cx', 'id'],
coupling_map=[[0, 1]],
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed))
with self.subTest(msg='test eigenvalue'):
self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy)
with self.subTest(msg='test dimension of optimal point'):
self.assertEqual(len(result.optimal_point), 16)
with self.subTest(msg='assert cost_function_evals is set'):
self.assertIsNotNone(result.cost_function_evals)
with self.subTest(msg='assert optimizer_time is set'):
self.assertIsNotNone(result.optimizer_time)
def test_circuit_input(self):
"""Test running the VQE on a plain QuantumCircuit object."""
wavefunction = QuantumCircuit(2).compose(EfficientSU2(2))
optimizer = SLSQP(maxiter=50)
vqe = VQE(self.h2_op, wavefunction, optimizer=optimizer)
result = vqe.run(self.statevector_simulator)
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=5)
@data(
(MatrixExpectation(), 1),
(AerPauliExpectation(), 1),
(PauliExpectation(), 2),
)
@unpack
def test_construct_circuit(self, expectation, num_circuits):
"""Test construct circuits returns QuantumCircuits and the right number of them."""
wavefunction = EfficientSU2(2, reps=1)
vqe = VQE(self.h2_op, wavefunction, expectation=expectation)
params = [0] * wavefunction.num_parameters
circuits = vqe.construct_circuit(params)
self.assertEqual(len(circuits), num_circuits)
for circuit in circuits:
self.assertIsInstance(circuit, QuantumCircuit)
def test_legacy_operator(self):
"""Test the VQE accepts and converts the legacy WeightedPauliOperator."""
pauli_dict = {
'paulis': [{
"coeff": {
"imag": 0.0,
"real": -1.052373245772859
},
"label": "II"
}, {
"coeff": {
"imag": 0.0,
"real": 0.39793742484318045
},
"label": "IZ"
}, {
"coeff": {
"imag": 0.0,
"real": -0.39793742484318045
},
"label": "ZI"
}, {
"coeff": {
"imag": 0.0,
"real": -0.01128010425623538
},
"label": "ZZ"
}, {
"coeff": {
"imag": 0.0,
"real": 0.18093119978423156
},
"label": "XX"
}]
}
h2_op = WeightedPauliOperator.from_dict(pauli_dict)
vqe = VQE(h2_op)
self.assertEqual(vqe.operator, self.h2_op)
def test_missing_varform_params(self):
"""Test specifying a variational form with no parameters raises an error."""
circuit = QuantumCircuit(self.h2_op.num_qubits)
vqe = VQE(self.h2_op, circuit)
with self.assertRaises(RuntimeError):
vqe.run(BasicAer.get_backend('statevector_simulator'))
@data(
(SLSQP(maxiter=50), 5, 4),
(SPSA(maxiter=150), 3, 2), # max_evals_grouped=n or =2 if n>2
)
@unpack
def test_max_evals_grouped(self, optimizer, places, max_evals_grouped):
""" VQE Optimizers test """
vqe = VQE(self.h2_op,
self.ryrz_wavefunction,
optimizer,
max_evals_grouped=max_evals_grouped,
quantum_instance=self.statevector_simulator)
result = vqe.run()
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=places)
def test_basic_aer_qasm(self):
"""Test the VQE on BasicAer's QASM simulator."""
optimizer = SPSA(maxiter=300, last_avg=5)
wavefunction = self.ry_wavefunction
vqe = VQE(self.h2_op, wavefunction, optimizer, max_evals_grouped=1)
# TODO benchmark this later.
result = vqe.run(self.qasm_simulator)
self.assertAlmostEqual(result.eigenvalue.real, -1.86823, places=2)
def test_with_aer_statevector(self):
"""Test VQE with Aer's statevector_simulator."""
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('statevector_simulator')
wavefunction = self.ry_wavefunction
optimizer = L_BFGS_B()
vqe = VQE(self.h2_op, wavefunction, optimizer, max_evals_grouped=1)
quantum_instance = QuantumInstance(
backend,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqe.run(quantum_instance)
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=6)
def test_with_aer_qasm(self):
"""Test VQE with Aer's qasm_simulator."""
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
optimizer = SPSA(maxiter=200, last_avg=5)
wavefunction = self.ry_wavefunction
vqe = VQE(self.h2_op,
wavefunction,
optimizer,
expectation=PauliExpectation())
quantum_instance = QuantumInstance(
backend,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqe.run(quantum_instance)
self.assertAlmostEqual(result.eigenvalue.real, -1.86305, places=2)
def test_with_aer_qasm_snapshot_mode(self):
"""Test the VQE using Aer's qasm_simulator snapshot mode."""
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
optimizer = L_BFGS_B()
wavefunction = self.ry_wavefunction
vqe = VQE(self.h2_op,
wavefunction,
optimizer,
expectation=AerPauliExpectation())
quantum_instance = QuantumInstance(
backend,
shots=1,
seed_simulator=aqua_globals.random_seed,
seed_transpiler=aqua_globals.random_seed)
result = vqe.run(quantum_instance)
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=6)
def test_callback(self):
"""Test the callback on VQE."""
history = {'eval_count': [], 'parameters': [], 'mean': [], 'std': []}
def store_intermediate_result(eval_count, parameters, mean, std):
history['eval_count'].append(eval_count)
history['parameters'].append(parameters)
history['mean'].append(mean)
history['std'].append(std)
optimizer = COBYLA(maxiter=3)
wavefunction = self.ry_wavefunction
vqe = VQE(self.h2_op,
wavefunction,
optimizer,
callback=store_intermediate_result)
vqe.run(self.qasm_simulator)
self.assertTrue(
all(isinstance(count, int) for count in history['eval_count']))
self.assertTrue(
all(isinstance(mean, float) for mean in history['mean']))
self.assertTrue(all(isinstance(std, float) for std in history['std']))
for params in history['parameters']:
self.assertTrue(all(isinstance(param, float) for param in params))
def test_reuse(self):
"""Test re-using a VQE algorithm instance."""
vqe = VQE()
with self.subTest(msg='assert running empty raises AquaError'):
with self.assertRaises(AquaError):
_ = vqe.run()
var_form = TwoLocal(rotation_blocks=['ry', 'rz'],
entanglement_blocks='cz')
vqe.var_form = var_form
with self.subTest(msg='assert missing operator raises AquaError'):
with self.assertRaises(AquaError):
_ = vqe.run()
vqe.operator = self.h2_op
with self.subTest(msg='assert missing backend raises AquaError'):
with self.assertRaises(AquaError):
_ = vqe.run()
vqe.quantum_instance = self.statevector_simulator
with self.subTest(msg='assert VQE works once all info is available'):
result = vqe.run()
self.assertAlmostEqual(result.eigenvalue.real,
self.h2_energy,
places=5)
operator = PrimitiveOp(
np.array([[1, 0, 0, 0], [0, -1, 0, 0], [0, 0, 2, 0], [0, 0, 0,
3]]))
with self.subTest(msg='assert minimum eigensolver interface works'):
result = vqe.compute_minimum_eigenvalue(operator)
self.assertAlmostEqual(result.eigenvalue.real, -1.0, places=5)
def test_vqe_optimizer(self):
""" Test running same VQE twice to re-use optimizer, then switch optimizer """
vqe = VQE(self.h2_op,
optimizer=SLSQP(),
quantum_instance=QuantumInstance(
BasicAer.get_backend('statevector_simulator')))
def run_check():
result = vqe.compute_minimum_eigenvalue()
self.assertAlmostEqual(result.eigenvalue.real,
-1.85727503,
places=5)
run_check()
with self.subTest('Optimizer re-use'):
run_check()
with self.subTest('Optimizer replace'):
vqe.optimizer = L_BFGS_B()
run_check()
def test_vqe_expectation_select(self):
"""Test expectation selection with Aer's qasm_simulator."""
try:
# pylint: disable=import-outside-toplevel
from qiskit import Aer
except Exception as ex: # pylint: disable=broad-except
self.skipTest(
"Aer doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
backend = Aer.get_backend('qasm_simulator')
with self.subTest('Defaults'):
vqe = VQE(self.h2_op, quantum_instance=backend)
self.assertIsInstance(vqe.expectation, PauliExpectation)
with self.subTest('Include custom'):
vqe = VQE(self.h2_op,
include_custom=True,
quantum_instance=backend)
self.assertIsInstance(vqe.expectation, AerPauliExpectation)
with self.subTest('Set explicitly'):
vqe = VQE(self.h2_op,
expectation=AerPauliExpectation(),
quantum_instance=backend)
self.assertIsInstance(vqe.expectation, AerPauliExpectation)
@unittest.skip(reason="IBMQ testing not available in general.")
def test_ibmq(self):
""" IBMQ VQE Test """
from qiskit import IBMQ
provider = IBMQ.load_account()
backend = provider.get_backend('ibmq_qasm_simulator')
ansatz = TwoLocal(rotation_blocks=['ry', 'rz'],
entanglement_blocks='cz')
opt = SLSQP(maxiter=1)
opt.set_max_evals_grouped(100)
vqe = VQE(self.h2_op, ansatz, SLSQP(maxiter=2))
result = vqe.run(backend)
print(result)
self.assertAlmostEqual(result.eigenvalue.real, self.h2_energy)
np.testing.assert_array_almost_equal(result.eigenvalue.real,
self.h2_energy, 5)
self.assertEqual(len(result.optimal_point), 16)
self.assertIsNotNone(result.cost_function_evals)
self.assertIsNotNone(result.optimizer_time)
@data(MatrixExpectation(), None)
def test_backend_change(self, user_expectation):
"""Test that VQE works when backend changes."""
vqe = VQE(
operator=self.h2_op,
var_form=TwoLocal(rotation_blocks=['ry', 'rz'],
entanglement_blocks='cz'),
optimizer=SLSQP(maxiter=2),
expectation=user_expectation,
quantum_instance=BasicAer.get_backend('statevector_simulator'))
result0 = vqe.run()
if user_expectation is not None:
with self.subTest('User expectation kept.'):
self.assertEqual(vqe.expectation, user_expectation)
else:
with self.subTest('Expectation created.'):
self.assertIsInstance(vqe.expectation, ExpectationBase)
try:
vqe.set_backend(BasicAer.get_backend('qasm_simulator'))
except Exception as ex: # pylint: disable=broad-except
self.fail("Failed to change backend. Error: '{}'".format(str(ex)))
return
result1 = vqe.run()
if user_expectation is not None:
with self.subTest(
'Change backend with user expectation, it is kept.'):
self.assertEqual(vqe.expectation, user_expectation)
else:
with self.subTest(
'Change backend without user expectation, one created.'):
self.assertIsInstance(vqe.expectation, ExpectationBase)
with self.subTest('Check results.'):
self.assertEqual(len(result0.optimal_point),
len(result1.optimal_point))
# Calculate the expectation value for Ising Hamiltonian
print('expectation_value:', psi.adjoint().compose(op).compose(psi).eval().real)
# %% another way of calculating expectation
# define your backend or quantum instance (where you can add settings)
backend = Aer.get_backend('qasm_simulator')
q_instance = QuantumInstance(backend, shots=1024)
# define the state to sample
measurable_expression = StateFn(op, is_measurement=True).compose(psi)
# convert to expectation value
expectation = PauliExpectation().convert(measurable_expression)
# get state sampler (you can also pass the backend directly)
sampler = CircuitSampler(q_instance).convert(expectation)
# evaluate
print('Sampled:', sampler.eval().real)
# %%
print(qc)
print('Hamiltonian of Ising model:')
print(H.print_details())
# %% Randomly sample Clifford circuits and calculate <H> for each one.
# (E.g. only change single qubit gates, not their locations in the circuit)
class TestCVaRExpectation(QiskitAquaTestCase):
"""Test the CVaR expectation object."""
def test_construction(self):
"""Test the correct operator expression is constructed."""
alpha = 0.5
base_expecation = PauliExpectation()
cvar_expecation = CVaRExpectation(alpha=alpha,
expectation=base_expecation)
with self.subTest('single operator'):
op = ~StateFn(Z) @ Plus
expected = CVaRMeasurement(Z, alpha) @ Plus
cvar = cvar_expecation.convert(op)
self.assertEqual(cvar, expected)
with self.subTest('list operator'):
op = ~StateFn(ListOp([Z ^ Z, I ^ Z])) @ (Plus ^ Plus)
expected = ListOp([
CVaRMeasurement((Z ^ Z), alpha) @ (Plus ^ Plus),
CVaRMeasurement((I ^ Z), alpha) @ (Plus ^ Plus)
])
cvar = cvar_expecation.convert(op)
self.assertEqual(cvar, expected)
def test_unsupported_expectation(self):
"""Assert passing an AerPauliExpectation raises an error."""
expecation = AerPauliExpectation()
with self.assertRaises(NotImplementedError):
_ = CVaRExpectation(alpha=1, expectation=expecation)
@data(PauliExpectation(), MatrixExpectation())
def test_underlying_expectation(self, base_expecation):
"""Test the underlying expectation works correctly."""
cvar_expecation = CVaRExpectation(alpha=0.3,
expectation=base_expecation)
circuit = QuantumCircuit(2)
circuit.z(0)
circuit.cp(0.5, 0, 1)
circuit.t(1)
op = ~StateFn(CircuitOp(circuit)) @ (Plus ^ 2)
cvar = cvar_expecation.convert(op)
expected = base_expecation.convert(op)
# test if the operators have been transformed in the same manner
self.assertEqual(cvar.oplist[0].primitive,
expected.oplist[0].primitive)
def test_compute_variance(self):
"""Test if the compute_variance method works"""
alphas = [0, .3, 0.5, 0.7, 1]
correct_vars = [0, 0, 0, 0.8163, 1]
for i, alpha in enumerate(alphas):
base_expecation = PauliExpectation()
cvar_expecation = CVaRExpectation(alpha=alpha,
expectation=base_expecation)
op = ~StateFn(Z ^ Z) @ (Plus ^ Plus)
cvar_var = cvar_expecation.compute_variance(op)
np.testing.assert_almost_equal(cvar_var,
correct_vars[i],
decimal=3)
