def test_measurement_error_mitigation_with_diff_qubit_order_ignis(
self, fitter_str):
"""measurement error mitigation with different qubit order"""
algorithm_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
fitter_cls = (CompleteMeasFitter_IG if fitter_str
== "CompleteMeasFitter" else TensoredMeasFitter_IG)
backend = Aer.get_backend("aer_simulator")
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=1679,
seed_transpiler=167,
shots=1000,
noise_model=noise_model,
measurement_error_mitigation_cls=fitter_cls,
cals_matrix_refresh_period=0,
)
# circuit
qc1 = QuantumCircuit(2, 2)
qc1.h(0)
qc1.cx(0, 1)
qc1.measure(0, 0)
qc1.measure(1, 1)
qc2 = QuantumCircuit(2, 2)
qc2.h(0)
qc2.cx(0, 1)
qc2.measure(1, 0)
qc2.measure(0, 1)
if fitter_cls == TensoredMeasFitter_IG:
with self.assertWarnsRegex(DeprecationWarning, r".*ignis.*"):
self.assertRaisesRegex(
QiskitError,
"TensoredMeasFitter doesn't support subset_fitter.",
quantum_instance.execute,
[qc1, qc2],
)
else:
# this should run smoothly
with self.assertWarnsRegex(DeprecationWarning, r".*ignis.*"):
quantum_instance.execute([qc1, qc2])
self.assertGreater(quantum_instance.time_taken, 0.0)
quantum_instance.reset_execution_results()
# failure case
qc3 = QuantumCircuit(3, 3)
qc3.h(2)
qc3.cx(1, 2)
qc3.measure(2, 1)
qc3.measure(1, 2)
self.assertRaises(QiskitError, quantum_instance.execute, [qc1, qc3])
def test_measurement_error_mitigation_with_diff_qubit_order(self):
"""measurement error mitigation with different qubit order"""
try:
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter
from qiskit import Aer
from qiskit.providers.aer import noise
except ImportError as ex:
self.skipTest(
"Package doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
algorithm_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend("aer_simulator")
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=1679,
seed_transpiler=167,
shots=1000,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
cals_matrix_refresh_period=0,
)
# circuit
qc1 = QuantumCircuit(2, 2)
qc1.h(0)
qc1.cx(0, 1)
qc1.measure(0, 0)
qc1.measure(1, 1)
qc2 = QuantumCircuit(2, 2)
qc2.h(0)
qc2.cx(0, 1)
qc2.measure(1, 0)
qc2.measure(0, 1)
# this should run smoothly
quantum_instance.execute([qc1, qc2])
self.assertGreater(quantum_instance.time_taken, 0.0)
quantum_instance.reset_execution_results()
# failure case
qc3 = QuantumCircuit(3, 3)
qc3.h(2)
qc3.cx(1, 2)
qc3.measure(2, 1)
qc3.measure(1, 2)
self.assertRaises(QiskitError, quantum_instance.execute, [qc1, qc3])
def test_w_backend_options(self):
""" with backend options test """
# run with backend_options
quantum_instance = QuantumInstance(self.backend, seed_transpiler=self.random_seed,
seed_simulator=self.random_seed, shots=1024,
backend_options={
'initial_statevector': [.5, .5, .5, .5]})
res_w_bo = quantum_instance.execute(self.qc).get_counts(self.qc)
self.assertGreaterEqual(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
quantum_instance.skip_qobj_validation = True
res_w_bo_skip_validation = quantum_instance.execute(self.qc).get_counts(self.qc)
self.assertGreaterEqual(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
self.assertTrue(_compare_dict(res_w_bo, res_w_bo_skip_validation))
def test_wo_backend_options(self):
""" without backend options test """
quantum_instance = QuantumInstance(self.backend,
seed_transpiler=self.random_seed,
seed_simulator=self.random_seed,
shots=1024)
# run without backend_options and without noise
res_wo_bo = quantum_instance.execute(self.qc).get_counts(self.qc)
self.assertGreaterEqual(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
quantum_instance.skip_qobj_validation = True
res_wo_bo_skip_validation = quantum_instance.execute(self.qc).get_counts(self.qc)
self.assertGreaterEqual(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
self.assertTrue(_compare_dict(res_wo_bo, res_wo_bo_skip_validation))
def test_measurement_error_mitigation_with_vqe(self):
""" measurement error mitigation test with vqe """
try:
# pylint: disable=import-outside-toplevel
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter
from qiskit import Aer
from qiskit.providers.aer import noise
except ImportError as ex: # pylint: disable=broad-except
self.skipTest("Package doesn't appear to be installed. Error: '{}'".format(str(ex)))
return
aqua_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1], [0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = Aer.get_backend('qasm_simulator')
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=167,
seed_transpiler=167,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter
)
h2_hamiltonian = -1.052373245772859 * (I ^ I) \
+ 0.39793742484318045 * (I ^ Z) \
- 0.39793742484318045 * (Z ^ I) \
- 0.01128010425623538 * (Z ^ Z) \
+ 0.18093119978423156 * (X ^ X)
optimizer = SPSA(maxiter=200)
var_form = EfficientSU2(2, reps=1)
vqe = VQE(
var_form=var_form,
operator=h2_hamiltonian,
quantum_instance=quantum_instance,
optimizer=optimizer,
)
result = vqe.compute_minimum_eigenvalue()
self.assertGreater(quantum_instance.time_taken, 0.)
quantum_instance.reset_execution_results()
self.assertAlmostEqual(result.eigenvalue.real, -1.86, places=2)
def test_measurement_error_mitigation_with_vqe(self):
"""measurement error mitigation test with vqe"""
try:
from qiskit.ignis.mitigation.measurement import CompleteMeasFitter
from qiskit.providers.aer import noise
except ImportError as ex:
self.skipTest(
"Package doesn't appear to be installed. Error: '{}'".format(
str(ex)))
return
algorithm_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
backend = self._qasm
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=167,
seed_transpiler=167,
noise_model=noise_model,
measurement_error_mitigation_cls=CompleteMeasFitter,
)
h2_hamiltonian = (-1.052373245772859 * (I ^ I) + 0.39793742484318045 *
(I ^ Z) - 0.39793742484318045 * (Z ^ I) -
0.01128010425623538 * (Z ^ Z) + 0.18093119978423156 *
(X ^ X))
optimizer = SPSA(maxiter=200)
ansatz = EfficientSU2(2, reps=1)
vqe = VQE(ansatz=ansatz,
optimizer=optimizer,
quantum_instance=quantum_instance)
result = vqe.compute_minimum_eigenvalue(operator=h2_hamiltonian)
self.assertGreater(quantum_instance.time_taken, 0.0)
quantum_instance.reset_execution_results()
self.assertAlmostEqual(result.eigenvalue.real, -1.86, delta=0.05)
def test_measurement_error_mitigation_with_vqe(self, config):
"""measurement error mitigation test with vqe"""
if _ERROR_MITIGATION_IMPORT_ERROR is not None:
self.skipTest(
f"Package doesn't appear to be installed. Error: '{_ERROR_MITIGATION_IMPORT_ERROR}'"
)
return
fitter_str, mit_pattern = config
algorithm_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
fitter_cls = (CompleteMeasFitter if fitter_str == "CompleteMeasFitter"
else TensoredMeasFitter)
backend = Aer.get_backend("aer_simulator")
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=167,
seed_transpiler=167,
noise_model=noise_model,
measurement_error_mitigation_cls=fitter_cls,
mit_pattern=mit_pattern,
)
h2_hamiltonian = (-1.052373245772859 * (I ^ I) + 0.39793742484318045 *
(I ^ Z) - 0.39793742484318045 * (Z ^ I) -
0.01128010425623538 * (Z ^ Z) + 0.18093119978423156 *
(X ^ X))
optimizer = SPSA(maxiter=200)
ansatz = EfficientSU2(2, reps=1)
vqe = VQE(ansatz=ansatz,
optimizer=optimizer,
quantum_instance=quantum_instance)
result = vqe.compute_minimum_eigenvalue(operator=h2_hamiltonian)
self.assertGreater(quantum_instance.time_taken, 0.0)
quantum_instance.reset_execution_results()
self.assertAlmostEqual(result.eigenvalue.real, -1.86, delta=0.05)
class TestBernoulli(QiskitAlgorithmsTestCase):
"""Tests based on the Bernoulli A operator.
This class tests
* the estimation result
* the constructed circuits
"""
def setUp(self):
super().setUp()
self._statevector = QuantumInstance(
backend=BasicAer.get_backend("statevector_simulator"),
seed_simulator=2,
seed_transpiler=2,
)
self._unitary = QuantumInstance(
backend=BasicAer.get_backend("unitary_simulator"),
shots=1,
seed_simulator=42,
seed_transpiler=91,
)
def qasm(shots=100):
return QuantumInstance(
backend=BasicAer.get_backend("qasm_simulator"),
shots=shots,
seed_simulator=2,
seed_transpiler=2,
)
self._qasm = qasm
@idata(
[
[0.2, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.2}],
[0.49, AmplitudeEstimation(3), {"estimation": 0.5, "mle": 0.49}],
[0.2, MaximumLikelihoodAmplitudeEstimation([0, 1, 2]), {"estimation": 0.2}],
[0.49, MaximumLikelihoodAmplitudeEstimation(3), {"estimation": 0.49}],
[0.2, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.2}],
[0.49, IterativeAmplitudeEstimation(0.001, 0.01), {"estimation": 0.49}],
[0.2, FasterAmplitudeEstimation(0.1, 3, rescale=False), {"estimation": 0.2}],
[0.12, FasterAmplitudeEstimation(0.1, 2, rescale=False), {"estimation": 0.12}],
]
)
@unpack
def test_statevector(self, prob, qae, expect):
"""statevector test"""
qae.quantum_instance = self._statevector
problem = EstimationProblem(BernoulliStateIn(prob), 0, BernoulliGrover(prob))
result = qae.estimate(problem)
self.assertGreaterEqual(self._statevector.time_taken, 0.0)
self._statevector.reset_execution_results()
for key, value in expect.items():
self.assertAlmostEqual(
value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
)
@idata(
[
[0.2, 100, AmplitudeEstimation(4), {"estimation": 0.14644, "mle": 0.193888}],
[0.0, 1000, AmplitudeEstimation(2), {"estimation": 0.0, "mle": 0.0}],
[
0.2,
100,
MaximumLikelihoodAmplitudeEstimation([0, 1, 2, 4, 8]),
{"estimation": 0.199606},
],
[0.8, 10, IterativeAmplitudeEstimation(0.1, 0.05), {"estimation": 0.811711}],
[0.2, 1000, FasterAmplitudeEstimation(0.1, 3, rescale=False), {"estimation": 0.198640}],
[
0.12,
100,
FasterAmplitudeEstimation(0.01, 3, rescale=False),
{"estimation": 0.119037},
],
]
)
@unpack
def test_qasm(self, prob, shots, qae, expect):
"""qasm test"""
qae.quantum_instance = self._qasm(shots)
problem = EstimationProblem(BernoulliStateIn(prob), [0], BernoulliGrover(prob))
result = qae.estimate(problem)
for key, value in expect.items():
self.assertAlmostEqual(
value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
)
@data(True, False)
def test_qae_circuit(self, efficient_circuit):
"""Test circuits resulting from canonical amplitude estimation.
Build the circuit manually and from the algorithm and compare the resulting unitaries.
"""
prob = 0.5
problem = EstimationProblem(BernoulliStateIn(prob), objective_qubits=[0])
for m in [2, 5]:
qae = AmplitudeEstimation(m)
angle = 2 * np.arcsin(np.sqrt(prob))
# manually set up the inefficient AE circuit
qr_eval = QuantumRegister(m, "a")
qr_objective = QuantumRegister(1, "q")
circuit = QuantumCircuit(qr_eval, qr_objective)
# initial Hadamard gates
for i in range(m):
circuit.h(qr_eval[i])
# A operator
circuit.ry(angle, qr_objective)
if efficient_circuit:
qae.grover_operator = BernoulliGrover(prob)
for power in range(m):
circuit.cry(2 * 2 ** power * angle, qr_eval[power], qr_objective[0])
else:
oracle = QuantumCircuit(1)
oracle.z(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
grover_op.global_phase = np.pi
for power in range(m):
circuit.compose(
grover_op.power(2 ** power).control(),
qubits=[qr_eval[power], qr_objective[0]],
inplace=True,
)
# fourier transform
iqft = QFT(m, do_swaps=False).inverse().reverse_bits()
circuit.append(iqft.to_instruction(), qr_eval)
actual_circuit = qae.construct_circuit(problem, measurement=False)
self.assertEqual(Operator(circuit), Operator(actual_circuit))
@data(True, False)
def test_iqae_circuits(self, efficient_circuit):
"""Test circuits resulting from iterative amplitude estimation.
Build the circuit manually and from the algorithm and compare the resulting unitaries.
"""
prob = 0.5
problem = EstimationProblem(BernoulliStateIn(prob), objective_qubits=[0])
for k in [2, 5]:
qae = IterativeAmplitudeEstimation(0.01, 0.05)
angle = 2 * np.arcsin(np.sqrt(prob))
# manually set up the inefficient AE circuit
q_objective = QuantumRegister(1, "q")
circuit = QuantumCircuit(q_objective)
# A operator
circuit.ry(angle, q_objective)
if efficient_circuit:
qae.grover_operator = BernoulliGrover(prob)
circuit.ry(2 * k * angle, q_objective[0])
else:
oracle = QuantumCircuit(1)
oracle.z(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
grover_op.global_phase = np.pi
for _ in range(k):
circuit.compose(grover_op, inplace=True)
actual_circuit = qae.construct_circuit(problem, k, measurement=False)
self.assertEqual(Operator(circuit), Operator(actual_circuit))
@data(True, False)
def test_mlae_circuits(self, efficient_circuit):
"""Test the circuits constructed for MLAE"""
prob = 0.5
problem = EstimationProblem(BernoulliStateIn(prob), objective_qubits=[0])
for k in [2, 5]:
qae = MaximumLikelihoodAmplitudeEstimation(k)
angle = 2 * np.arcsin(np.sqrt(prob))
# compute all the circuits used for MLAE
circuits = []
# 0th power
q_objective = QuantumRegister(1, "q")
circuit = QuantumCircuit(q_objective)
circuit.ry(angle, q_objective)
circuits += [circuit]
# powers of 2
for power in range(k):
q_objective = QuantumRegister(1, "q")
circuit = QuantumCircuit(q_objective)
# A operator
circuit.ry(angle, q_objective)
# Q^(2^j) operator
if efficient_circuit:
qae.grover_operator = BernoulliGrover(prob)
circuit.ry(2 * 2 ** power * angle, q_objective[0])
else:
oracle = QuantumCircuit(1)
oracle.z(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
grover_op.global_phase = np.pi
for _ in range(2 ** power):
circuit.compose(grover_op, inplace=True)
circuits += [circuit]
actual_circuits = qae.construct_circuits(problem, measurement=False)
for actual, expected in zip(actual_circuits, circuits):
self.assertEqual(Operator(actual), Operator(expected))
class TestSineIntegral(QiskitAlgorithmsTestCase):
"""Tests based on the A operator to integrate sin^2(x).
This class tests
* the estimation result
* the confidence intervals
"""
def setUp(self):
super().setUp()
self._statevector = QuantumInstance(
backend=BasicAer.get_backend("statevector_simulator"),
seed_simulator=123,
seed_transpiler=41,
)
def qasm(shots=100):
return QuantumInstance(
backend=BasicAer.get_backend("qasm_simulator"),
shots=shots,
seed_simulator=7192,
seed_transpiler=90000,
)
self._qasm = qasm
@idata(
[
[2, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.270290}],
[4, MaximumLikelihoodAmplitudeEstimation(4), {"estimation": 0.272675}],
[3, IterativeAmplitudeEstimation(0.1, 0.1), {"estimation": 0.272082}],
[3, FasterAmplitudeEstimation(0.01, 1), {"estimation": 0.272082}],
]
)
@unpack
def test_statevector(self, n, qae, expect):
"""Statevector end-to-end test"""
# construct factories for A and Q
# qae.state_preparation = SineIntegral(n)
qae.quantum_instance = self._statevector
estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
# result = qae.run(self._statevector)
result = qae.estimate(estimation_problem)
self.assertGreaterEqual(self._statevector.time_taken, 0.0)
self._statevector.reset_execution_results()
for key, value in expect.items():
self.assertAlmostEqual(
value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
)
@idata(
[
[4, 10, AmplitudeEstimation(2), {"estimation": 0.5, "mle": 0.333333}],
[3, 10, MaximumLikelihoodAmplitudeEstimation(2), {"estimation": 0.256878}],
[3, 1000, IterativeAmplitudeEstimation(0.01, 0.01), {"estimation": 0.271790}],
[3, 1000, FasterAmplitudeEstimation(0.1, 4), {"estimation": 0.274168}],
]
)
@unpack
def test_qasm(self, n, shots, qae, expect):
"""QASM simulator end-to-end test."""
# construct factories for A and Q
qae.quantum_instance = self._qasm(shots)
estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
result = qae.estimate(estimation_problem)
for key, value in expect.items():
self.assertAlmostEqual(
value, getattr(result, key), places=3, msg=f"estimate `{key}` failed"
)
@idata(
[
[
AmplitudeEstimation(3),
"mle",
{
"likelihood_ratio": (0.2494734, 0.3003771),
"fisher": (0.2486176, 0.2999286),
"observed_fisher": (0.2484562, 0.3000900),
},
],
[
MaximumLikelihoodAmplitudeEstimation(3),
"estimation",
{
"likelihood_ratio": (0.2598794, 0.2798536),
"fisher": (0.2584889, 0.2797018),
"observed_fisher": (0.2659279, 0.2722627),
},
],
]
)
@unpack
def test_confidence_intervals(self, qae, key, expect):
"""End-to-end test for all confidence intervals."""
n = 3
qae.quantum_instance = self._statevector
estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
# statevector simulator
result = qae.estimate(estimation_problem)
self.assertGreater(self._statevector.time_taken, 0.0)
self._statevector.reset_execution_results()
methods = ["lr", "fi", "oi"] # short for likelihood_ratio, fisher, observed_fisher
alphas = [0.1, 0.00001, 0.9] # alpha shouldn't matter in statevector
for alpha, method in zip(alphas, methods):
confint = qae.compute_confidence_interval(result, alpha, method)
# confidence interval based on statevector should be empty, as we are sure of the result
self.assertAlmostEqual(confint[1] - confint[0], 0.0)
self.assertAlmostEqual(confint[0], getattr(result, key))
# qasm simulator
shots = 100
alpha = 0.01
qae.quantum_instance = self._qasm(shots)
result = qae.estimate(estimation_problem)
for method, expected_confint in expect.items():
confint = qae.compute_confidence_interval(result, alpha, method)
np.testing.assert_array_almost_equal(confint, expected_confint)
self.assertTrue(confint[0] <= getattr(result, key) <= confint[1])
def test_iqae_confidence_intervals(self):
"""End-to-end test for the IQAE confidence interval."""
n = 3
qae = IterativeAmplitudeEstimation(0.1, 0.01, quantum_instance=self._statevector)
expected_confint = (0.1984050, 0.3511015)
estimation_problem = EstimationProblem(SineIntegral(n), objective_qubits=[n])
# statevector simulator
result = qae.estimate(estimation_problem)
self.assertGreaterEqual(self._statevector.time_taken, 0.0)
self._statevector.reset_execution_results()
confint = result.confidence_interval
# confidence interval based on statevector should be empty, as we are sure of the result
self.assertAlmostEqual(confint[1] - confint[0], 0.0)
self.assertAlmostEqual(confint[0], result.estimation)
# qasm simulator
shots = 100
qae.quantum_instance = self._qasm(shots)
result = qae.estimate(estimation_problem)
confint = result.confidence_interval
np.testing.assert_array_almost_equal(confint, expected_confint)
self.assertTrue(confint[0] <= result.estimation <= confint[1])
def test_measurement_error_mitigation_with_diff_qubit_order(
self,
fitter_str,
mit_pattern,
fails,
):
"""measurement error mitigation with different qubit order"""
algorithm_globals.random_seed = 0
# build noise model
noise_model = noise.NoiseModel()
read_err = noise.errors.readout_error.ReadoutError([[0.9, 0.1],
[0.25, 0.75]])
noise_model.add_all_qubit_readout_error(read_err)
fitter_cls = (CompleteMeasFitter if fitter_str == "CompleteMeasFitter"
else TensoredMeasFitter)
backend = Aer.get_backend("aer_simulator")
quantum_instance = QuantumInstance(
backend=backend,
seed_simulator=1679,
seed_transpiler=167,
shots=1000,
noise_model=noise_model,
measurement_error_mitigation_cls=fitter_cls,
cals_matrix_refresh_period=0,
mit_pattern=mit_pattern,
)
# circuit
qc1 = QuantumCircuit(2, 2)
qc1.h(0)
qc1.cx(0, 1)
qc1.measure(0, 0)
qc1.measure(1, 1)
qc2 = QuantumCircuit(2, 2)
qc2.h(0)
qc2.cx(0, 1)
qc2.measure(1, 0)
qc2.measure(0, 1)
if fails:
self.assertRaisesRegex(
QiskitError,
"Each element in the mit pattern should have length 1.",
quantum_instance.execute,
[qc1, qc2],
)
else:
quantum_instance.execute([qc1, qc2])
self.assertGreater(quantum_instance.time_taken, 0.0)
quantum_instance.reset_execution_results()
# failure case
qc3 = QuantumCircuit(3, 3)
qc3.h(2)
qc3.cx(1, 2)
qc3.measure(2, 1)
qc3.measure(1, 2)
self.assertRaises(QiskitError, quantum_instance.execute, [qc1, qc3])