def test_application(self):
"""Test an end-to-end application."""
bounds = np.array([0., 7.])
num_qubits = 3
# the distribution circuit is a normal distribution plus a QGAN-trained ansatz circuit
dist = NormalDistribution(num_qubits, mu=1, sigma=1, bounds=bounds)
ansatz = TwoLocal(num_qubits, 'ry', 'cz', reps=1, entanglement='circular')
trained_params = [0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428, 0.13537225]
ansatz.assign_parameters(trained_params, inplace=True)
dist.compose(ansatz, inplace=True)
# create the European call expected value
strike_price = 2
rescaling_factor = 0.25
european_call = EuropeanCallExpectedValue(num_qubits, strike_price, rescaling_factor,
bounds)
# create the state preparation circuit
state_preparation = european_call.compose(dist, front=True)
iae = IterativeAmplitudeEstimation(0.01, 0.05, state_preparation=state_preparation,
objective_qubits=[num_qubits],
post_processing=european_call.post_processing)
backend = QuantumInstance(Aer.get_backend('qasm_simulator'),
seed_simulator=125, seed_transpiler=80)
result = iae.run(backend)
self.assertAlmostEqual(result.estimation, 1.0364776997977694)
def test_application(self):
"""Test an end-to-end application."""
try:
from qiskit import Aer # pylint: disable=unused-import,import-outside-toplevel
except ImportError as ex: # pylint: disable=broad-except
self.skipTest("Aer doesn't appear to be installed. Error: '{}'".format(str(ex)))
return
bounds = np.array([0., 7.])
num_qubits = 3
# the distribution circuit is a normal distribution plus a QGAN-trained ansatz circuit
dist = NormalDistribution(num_qubits, mu=1, sigma=1, bounds=bounds)
ansatz = TwoLocal(num_qubits, 'ry', 'cz', reps=1, entanglement='circular')
trained_params = [0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428, 0.13537225]
ansatz.assign_parameters(trained_params, inplace=True)
dist.compose(ansatz, inplace=True)
# create the European call expected value
strike_price = 2
rescaling_factor = 0.25
european_call = EuropeanCallPricingObjective(num_state_qubits=num_qubits,
strike_price=strike_price,
rescaling_factor=rescaling_factor,
bounds=bounds)
# create the state preparation circuit
state_preparation = european_call.compose(dist, front=True)
problem = EstimationProblem(state_preparation=state_preparation,
objective_qubits=[num_qubits],
post_processing=european_call.post_processing)
q_i = QuantumInstance(Aer.get_backend('qasm_simulator'),
seed_simulator=125, seed_transpiler=80)
iae = IterativeAmplitudeEstimation(epsilon_target=0.01,
alpha=0.05,
quantum_instance=q_i)
result = iae.estimate(problem)
self.assertAlmostEqual(result.estimation_processed, 1.0127253837345427)
