class TestBernoulli(QiskitAquaTestCase):
""" Test Bernoulli """
def setUp(self):
super().setUp()
self._statevector = QuantumInstance(
backend=BasicAer.get_backend('statevector_simulator'),
seed_simulator=2,
seed_transpiler=2)
def qasm(shots=100):
return QuantumInstance(
backend=BasicAer.get_backend('qasm_simulator'),
shots=shots,
seed_simulator=2,
seed_transpiler=2)
self._qasm = qasm
@parameterized.expand(
[[0.2, AmplitudeEstimation(2), {
'estimation': 0.5,
'mle': 0.2
}], [0.4,
AmplitudeEstimation(4), {
'estimation': 0.30866,
'mle': 0.4
}],
[0.82,
AmplitudeEstimation(5), {
'estimation': 0.85355,
'mle': 0.82
}], [0.49,
AmplitudeEstimation(3), {
'estimation': 0.5,
'mle': 0.49
}],
[0.2,
MaximumLikelihoodAmplitudeEstimation(2), {
'estimation': 0.2
}],
[0.4,
MaximumLikelihoodAmplitudeEstimation(4), {
'estimation': 0.4
}],
[0.82,
MaximumLikelihoodAmplitudeEstimation(5), {
'estimation': 0.82
}],
[0.49,
MaximumLikelihoodAmplitudeEstimation(3), {
'estimation': 0.49
}],
[0.2,
IterativeAmplitudeEstimation(0.1, 0.1), {
'estimation': 0.2
}],
[
0.4,
IterativeAmplitudeEstimation(0.00001, 0.01), {
'estimation': 0.4
}
],
[
0.82,
IterativeAmplitudeEstimation(0.00001, 0.05), {
'estimation': 0.82
}
],
[
0.49,
IterativeAmplitudeEstimation(0.001, 0.01), {
'estimation': 0.49
}
]])
def test_statevector(self, prob, a_e, expect):
""" statevector test """
# construct factories for A and Q
a_e.a_factory = BernoulliAFactory(prob)
a_e.q_factory = BernoulliQFactory(a_e.a_factory)
result = a_e.run(self._statevector)
for key, value in expect.items():
self.assertAlmostEqual(value,
result[key],
places=3,
msg="estimate `{}` failed".format(key))
@parameterized.expand([[
0.2, 100,
AmplitudeEstimation(4), {
'estimation': 0.14644,
'mle': 0.193888
}
], [0.0, 1000,
AmplitudeEstimation(2), {
'estimation': 0.0,
'mle': 0.0
}],
[
0.8, 10,
AmplitudeEstimation(7), {
'estimation': 0.79784,
'mle': 0.801612
}
],
[
0.2, 100,
MaximumLikelihoodAmplitudeEstimation(4), {
'estimation': 0.199606
}
],
[
0.4, 1000,
MaximumLikelihoodAmplitudeEstimation(6), {
'estimation': 0.399488
}
],
[
0.8, 10,
MaximumLikelihoodAmplitudeEstimation(7), {
'estimation': 0.800926
}
],
[
0.2, 100,
IterativeAmplitudeEstimation(0.0001, 0.01), {
'estimation': 0.199987
}
],
[
0.4, 1000,
IterativeAmplitudeEstimation(0.001, 0.05), {
'estimation': 0.400071
}
],
[
0.8, 10,
IterativeAmplitudeEstimation(0.1, 0.05), {
'estimation': 0.811711
}
]])
def test_qasm(self, prob, shots, a_e, expect):
""" qasm test """
# construct factories for A and Q
a_e.a_factory = BernoulliAFactory(prob)
a_e.q_factory = BernoulliQFactory(a_e.a_factory)
result = a_e.run(self._qasm(shots))
for key, value in expect.items():
self.assertAlmostEqual(value,
result[key],
places=3,
msg="estimate `{}` failed".format(key))
class TestBernoulli(QiskitAquaTestCase):
"""Tests based on the Bernoulli A operator.
This class tests
* the estimation result
* the constructed circuits
"""
def setUp(self):
super().setUp()
warnings.filterwarnings(action="ignore", category=DeprecationWarning)
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
def tearDown(self):
super().tearDown()
warnings.filterwarnings(action="always", category=DeprecationWarning)
@idata([
[0.2, AmplitudeEstimation(2), {'estimation': 0.5, 'mle': 0.2}],
[0.4, AmplitudeEstimation(4), {'estimation': 0.30866, 'mle': 0.4}],
[0.82, AmplitudeEstimation(5), {'estimation': 0.85355, 'mle': 0.82}],
[0.49, AmplitudeEstimation(3), {'estimation': 0.5, 'mle': 0.49}],
[0.2, MaximumLikelihoodAmplitudeEstimation(2), {'estimation': 0.2}],
[0.4, MaximumLikelihoodAmplitudeEstimation(4), {'estimation': 0.4}],
[0.82, MaximumLikelihoodAmplitudeEstimation(5), {'estimation': 0.82}],
[0.49, MaximumLikelihoodAmplitudeEstimation(3), {'estimation': 0.49}],
[0.2, IterativeAmplitudeEstimation(0.1, 0.1), {'estimation': 0.2}],
[0.4, IterativeAmplitudeEstimation(0.00001, 0.01), {'estimation': 0.4}],
[0.82, IterativeAmplitudeEstimation(0.00001, 0.05), {'estimation': 0.82}],
[0.49, IterativeAmplitudeEstimation(0.001, 0.01), {'estimation': 0.49}]
])
@unpack
def test_statevector(self, prob, qae, expect):
"""Test running QAE using the statevector simulator."""
# construct factories for A and Q
qae.a_factory = BernoulliAFactory(prob)
qae.q_factory = BernoulliQFactory(qae.a_factory)
result = qae.run(self._statevector)
for key, value in expect.items():
self.assertAlmostEqual(value, result[key], places=3,
msg="estimate `{}` failed".format(key))
@idata([
[0.2, 100, AmplitudeEstimation(4), {'estimation': 0.14644, 'mle': 0.193888}],
[0.0, 1000, AmplitudeEstimation(2), {'estimation': 0.0, 'mle': 0.0}],
[0.8, 10, AmplitudeEstimation(7), {'estimation': 0.79784, 'mle': 0.801612}],
[0.2, 100, MaximumLikelihoodAmplitudeEstimation(4), {'estimation': 0.199606}],
[0.4, 1000, MaximumLikelihoodAmplitudeEstimation(6), {'estimation': 0.399488}],
# [0.8, 10, MaximumLikelihoodAmplitudeEstimation(7), {'estimation': 0.800926}],
[0.2, 100, IterativeAmplitudeEstimation(0.0001, 0.01), {'estimation': 0.199987}],
[0.4, 1000, IterativeAmplitudeEstimation(0.001, 0.05), {'estimation': 0.400071}],
[0.8, 10, IterativeAmplitudeEstimation(0.1, 0.05), {'estimation': 0.811711}]
])
@unpack
def test_qasm(self, prob, shots, qae, expect):
""" qasm test """
# construct factories for A and Q
qae.a_factory = BernoulliAFactory(prob)
qae.q_factory = BernoulliQFactory(qae.a_factory)
result = qae.run(self._qasm(shots))
for key, value in expect.items():
self.assertAlmostEqual(value, result[key], places=3,
msg="estimate `{}` failed".format(key))
@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
for m in range(2, 7):
qae = AmplitudeEstimation(m, a_factory=BernoulliAFactory(prob))
angle = 2 * np.arcsin(np.sqrt(prob))
# manually set up the inefficient AE circuit
q_ancilla = QuantumRegister(m, 'a')
q_objective = QuantumRegister(1, 'q')
circuit = QuantumCircuit(q_ancilla, q_objective)
# initial Hadamard gates
for i in range(m):
circuit.h(q_ancilla[i])
# A operator
circuit.ry(angle, q_objective)
if efficient_circuit:
qae.q_factory = BernoulliQFactory(qae.a_factory)
for power in range(m):
circuit.cry(2 * 2 ** power * angle, q_ancilla[power], q_objective[0])
else:
q_factory = QFactory(qae.a_factory, i_objective=0)
for power in range(m):
for _ in range(2**power):
q_factory.build_controlled(circuit, q_objective, q_ancilla[power])
# fourier transform
iqft = QFT(m, do_swaps=False).inverse()
circuit.append(iqft.to_instruction(), q_ancilla)
expected_unitary = self._unitary.execute(circuit).get_unitary()
actual_circuit = qae.construct_circuit(measurement=False)
actual_unitary = self._unitary.execute(actual_circuit).get_unitary()
diff = np.sum(np.abs(actual_unitary - expected_unitary))
self.assertAlmostEqual(diff, 0)
@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
for k in range(2, 7):
qae = IterativeAmplitudeEstimation(0.01, 0.05, a_factory=BernoulliAFactory(prob))
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.q_factory = BernoulliQFactory(qae.a_factory)
# for power in range(k):
# circuit.ry(2 ** power * angle, q_objective[0])
circuit.ry(2 * k * angle, q_objective[0])
else:
q_factory = QFactory(qae.a_factory, i_objective=0)
for _ in range(k):
q_factory.build(circuit, q_objective)
expected_unitary = self._unitary.execute(circuit).get_unitary()
actual_circuit = qae.construct_circuit(k, measurement=False)
actual_unitary = self._unitary.execute(actual_circuit).get_unitary()
diff = np.sum(np.abs(actual_unitary - expected_unitary))
self.assertAlmostEqual(diff, 0)
@data(True, False)
def test_mlae_circuits(self, efficient_circuit):
""" Test the circuits constructed for MLAE """
prob = 0.5
for k in range(1, 7):
qae = MaximumLikelihoodAmplitudeEstimation(k, a_factory=BernoulliAFactory(prob))
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.q_factory = BernoulliQFactory(qae.a_factory)
circuit.ry(2 * 2 ** power * angle, q_objective[0])
else:
q_factory = QFactory(qae.a_factory, i_objective=0)
for _ in range(2**power):
q_factory.build(circuit, q_objective)
actual_circuits = qae.construct_circuits(measurement=False)
for actual, expected in zip(actual_circuits, circuits):
expected_unitary = self._unitary.execute(expected).get_unitary()
actual_unitary = self._unitary.execute(actual).get_unitary()
diff = np.sum(np.abs(actual_unitary - expected_unitary))
self.assertAlmostEqual(diff, 0)
class TestSineIntegral(QiskitAquaTestCase):
"""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
}],
])
@unpack
def test_statevector(self, n, qae, expect):
""" Statevector end-to-end test """
# construct factories for A and Q
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
qae.a_factory = SineIntegralAFactory(n)
result = qae.run(self._statevector)
for key, value in expect.items():
self.assertAlmostEqual(value,
result[key],
places=3,
msg="estimate `{}` failed".format(key))
@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
}
],
])
@unpack
def test_qasm(self, n, shots, qae, expect):
"""QASM simulator end-to-end test."""
# construct factories for A and Q
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
qae.a_factory = SineIntegralAFactory(n)
result = qae.run(self._qasm(shots))
for key, value in expect.items():
self.assertAlmostEqual(value,
result[key],
places=3,
msg="estimate `{}` failed".format(key))
@idata([
[
AmplitudeEstimation(3), 'mle', {
'likelihood_ratio': [0.24947346406470136, 0.3003771197734433],
'fisher': [0.24861769995820207, 0.2999286066724035],
'observed_fisher': [0.24845622030041542, 0.30009008633019013]
}
],
[
MaximumLikelihoodAmplitudeEstimation(3), 'estimation', {
'likelihood_ratio': [0.25987941798909114, 0.27985361366769945],
'fisher': [0.2584889015125656, 0.2797018754936686],
'observed_fisher': [0.2659279996107888, 0.2722627773954454]
}
],
])
@unpack
def test_confidence_intervals(self, qae, key, expect):
"""End-to-end test for all confidence intervals."""
n = 3
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
qae.a_factory = SineIntegralAFactory(n)
# statevector simulator
result = qae.run(self._statevector)
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.confidence_interval(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], result[key])
# qasm simulator
shots = 100
alpha = 0.01
result = qae.run(self._qasm(shots))
for method, expected_confint in expect.items():
confint = qae.confidence_interval(alpha, method)
np.testing.assert_almost_equal(confint,
expected_confint,
decimal=10)
self.assertTrue(confint[0] <= result[key] <= confint[1])
def test_iqae_confidence_intervals(self):
"""End-to-end test for the IQAE confidence interval."""
n = 3
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
qae = IterativeAmplitudeEstimation(
0.1, 0.01, a_factory=SineIntegralAFactory(n))
expected_confint = [0.19840508760087738, 0.35110155403424115]
# statevector simulator
result = qae.run(self._statevector)
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
result = qae.run(self._qasm(shots))
confint = result['confidence_interval']
self.assertEqual(confint, expected_confint)
self.assertTrue(confint[0] <= result['estimation'] <= confint[1])
class TestProblemSetting(QiskitAquaTestCase):
"""Test the setting and getting of the A and Q operator and the objective qubit index."""
def setUp(self):
super().setUp()
self.a_bernoulli = BernoulliAFactory(0)
self.q_bernoulli = BernoulliQFactory(self.a_bernoulli)
self.i_bernoulli = 0
num_qubits = 5
self.a_integral = SineIntegralAFactory(num_qubits)
with warnings.catch_warnings():
warnings.filterwarnings('ignore', category=DeprecationWarning)
self.q_integral = QFactory(self.a_integral, num_qubits)
self.i_integral = num_qubits
@idata([
[AmplitudeEstimation(2)],
[IterativeAmplitudeEstimation(0.1, 0.001)],
[MaximumLikelihoodAmplitudeEstimation(3)],
])
@unpack
def test_operators(self, qae):
""" Test if A/Q operator + i_objective set correctly """
warnings.filterwarnings('ignore', category=DeprecationWarning)
self.assertIsNone(qae.a_factory)
self.assertIsNone(qae.q_factory)
self.assertIsNone(qae.i_objective)
self.assertIsNone(qae._a_factory)
self.assertIsNone(qae._q_factory)
self.assertIsNone(qae._i_objective)
qae.a_factory = self.a_bernoulli
self.assertIsNotNone(qae.a_factory)
self.assertIsNotNone(qae.q_factory)
self.assertIsNotNone(qae.i_objective)
self.assertIsNotNone(qae._a_factory)
self.assertIsNone(qae._q_factory)
self.assertIsNone(qae._i_objective)
qae.q_factory = self.q_bernoulli
self.assertIsNotNone(qae.a_factory)
self.assertIsNotNone(qae.q_factory)
self.assertIsNotNone(qae.i_objective)
self.assertIsNotNone(qae._a_factory)
self.assertIsNotNone(qae._q_factory)
self.assertIsNone(qae._i_objective)
qae.i_objective = self.i_bernoulli
self.assertIsNotNone(qae.a_factory)
self.assertIsNotNone(qae.q_factory)
self.assertIsNotNone(qae.i_objective)
self.assertIsNotNone(qae._a_factory)
self.assertIsNotNone(qae._q_factory)
self.assertIsNotNone(qae._i_objective)
warnings.filterwarnings('always', category=DeprecationWarning)
@data(
AmplitudeEstimation(2),
IterativeAmplitudeEstimation(0.1, 0.001),
MaximumLikelihoodAmplitudeEstimation(3),
)
def test_a_factory_update(self, qae):
"""Test if the Q factory is updated if the a_factory changes -- except set manually."""
# Case 1: Set to BernoulliAFactory with default Q operator
warnings.filterwarnings(action="ignore", category=DeprecationWarning)
qae.a_factory = self.a_bernoulli
self.assertIsInstance(qae.q_factory.a_factory, BernoulliAFactory)
self.assertEqual(qae.i_objective, self.i_bernoulli)
# Case 2: Change to SineIntegralAFactory with default Q operator
qae.a_factory = self.a_integral
self.assertIsInstance(qae.q_factory.a_factory, SineIntegralAFactory)
self.assertEqual(qae.i_objective, self.i_integral)
# Case 3: Set to BernoulliAFactory with special Q operator
qae.a_factory = self.a_bernoulli
qae.q_factory = self.q_bernoulli
self.assertIsInstance(qae.q_factory, BernoulliQFactory)
self.assertEqual(qae.i_objective, self.i_bernoulli)
# Case 4: Set to SineIntegralAFactory, and do not set Q. Then the old Q operator
# should remain
qae.a_factory = self.a_integral
self.assertIsInstance(qae.q_factory, BernoulliQFactory)
self.assertEqual(qae.i_objective, self.i_bernoulli)
warnings.filterwarnings(action="always", category=DeprecationWarning)
class TestBernoulli(QiskitAquaTestCase):
"""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(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
}
]])
@unpack
def test_statevector(self, prob, qae, expect):
""" statevector test """
# construct factories for A and Q
qae.state_preparation = BernoulliStateIn(prob)
qae.grover_operator = BernoulliGrover(prob)
result = qae.run(self._statevector)
for key, value in expect.items():
self.assertAlmostEqual(value,
getattr(result, key),
places=3,
msg="estimate `{}` failed".format(key))
@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(4), {
'estimation': 0.199606
}
],
[
0.8, 10,
IterativeAmplitudeEstimation(0.1, 0.05), {
'estimation': 0.811711
}
]])
@unpack
def test_qasm(self, prob, shots, qae, expect):
""" qasm test """
# construct factories for A and Q
qae.state_preparation = BernoulliStateIn(prob)
qae.grover_operator = BernoulliGrover(prob)
result = qae.run(self._qasm(shots))
for key, value in expect.items():
self.assertAlmostEqual(value,
getattr(result, key),
places=3,
msg="estimate `{}` failed".format(key))
@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
for m in [2, 5]:
qae = AmplitudeEstimation(m, BernoulliStateIn(prob))
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.x(0)
oracle.z(0)
oracle.x(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
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()
circuit.append(iqft.to_instruction(), qr_eval)
actual_circuit = qae.construct_circuit(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
for k in [2, 5]:
qae = IterativeAmplitudeEstimation(
0.01, 0.05, state_preparation=BernoulliStateIn(prob))
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.x(0)
oracle.z(0)
oracle.x(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
for _ in range(k):
circuit.compose(grover_op, inplace=True)
actual_circuit = qae.construct_circuit(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
for k in [2, 5]:
qae = MaximumLikelihoodAmplitudeEstimation(
k, state_preparation=BernoulliStateIn(prob))
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.x(0)
oracle.z(0)
oracle.x(0)
state_preparation = QuantumCircuit(1)
state_preparation.ry(angle, 0)
grover_op = GroverOperator(oracle, state_preparation)
for _ in range(2**power):
circuit.compose(grover_op, inplace=True)
actual_circuits = qae.construct_circuits(measurement=False)
for actual, expected in zip(actual_circuits, circuits):
self.assertEqual(Operator(actual), Operator(expected))
plt.show()
# evaluate exact expected value (normalized to the [0, 1] interval)
exact_value = np.dot(uncertainty_model.probabilities, y)
exact_delta = sum(uncertainty_model.probabilities[np.logical_and(
x >= strike_price_1, x <= strike_price_2)])
print('exact expected value:\t%.4f' % exact_value)
print('exact delta value: \t%.4f' % exact_delta)
# set target precision and confidence level
epsilon = 0.01
alpha = 0.05
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(epsilon=epsilon,
alpha=alpha,
a_factory=bull_spread)
result = ae.run(quantum_instance=Aer.get_backend('qasm_simulator'), shots=100)
conf_int = np.array(result['confidence_interval'])
print('Exact value: \t%.4f' % exact_value)
print('Estimated value:\t%.4f' % result['estimation'])
print('Confidence interval: \t[%.4f, %.4f]' % tuple(conf_int))
###Evaluate Delta which is a little simplier than the Excpected Payoff
# setup piecewise linear objective fcuntion
breakpoints = [uncertainty_model.low, strike_price_1, strike_price_2]
slopes = [0, 0, 0]
offsets = [0, 1, 0]
f_min = 0
plt.plot(x, y_strike, 'ro-')
plt.grid()
plt.title('Payoff Function', size=15)
plt.xlabel('Spot Price', size=15)
plt.ylabel('Payoff', size=15)
plt.xticks(x, size=15, rotation=90)
plt.yticks(size=15)
plt.show()
# set target precision and confidence level
epsilon = 0.01
alpha = 0.05
# construct amplitude estimation
ae = IterativeAmplitudeEstimation(epsilon=epsilon, alpha=alpha, a_factory=european_call)
result = ae.run(quantum_instance=Aer.get_backend('qasm_simulator'), shots=100)
conf_int = np.array(result['confidence_interval'])
print('Exact value: \t%.4f' % ep_trained)
print('Estimated value: \t%.4f' % (result['estimation']))
print('Confidence interval:\t[%.4f, %.4f]' % tuple(conf_int))
import qiskit.tools.jupyter
#%qiskit_version_table
#%qiskit_copyright