def test_readme_sample(self):
"""readme sample test"""
# pylint: disable=import-outside-toplevel,redefined-builtin
def print(*args):
"""overloads print to log values"""
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
import numpy as np
from qiskit import BasicAer
from qiskit.algorithms import AmplitudeEstimation
from qiskit.circuit.library import NormalDistribution
from qiskit_finance.applications import FixedIncomePricing
# Create a suitable multivariate distribution
num_qubits = [2, 2]
bounds = [(0, 0.12), (0, 0.24)]
mvnd = NormalDistribution(num_qubits,
mu=[0.12, 0.24],
sigma=0.01 * np.eye(2),
bounds=bounds)
# Create fixed income component
fixed_income = FixedIncomePricing(
num_qubits,
np.eye(2),
np.zeros(2),
cash_flow=[1.0, 2.0],
rescaling_factor=0.125,
bounds=bounds,
uncertainty_model=mvnd,
)
# the FixedIncomeExpectedValue provides us with the necessary rescalings
# create the A operator for amplitude estimation
problem = fixed_income.to_estimation_problem()
# Set number of evaluation qubits (samples)
num_eval_qubits = 5
# Construct and run amplitude estimation
q_i = BasicAer.get_backend("statevector_simulator")
algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits,
quantum_instance=q_i)
result = algo.estimate(problem)
print("Estimated value:\t%.4f" % fixed_income.interpret(result))
print("Probability: \t%.4f" % result.max_probability)
# ----------------------------------------------------------------------
with self.subTest("test estimation"):
self.assertAlmostEqual(result.estimation_processed, 2.46, places=4)
with self.subTest("test max.probability"):
self.assertAlmostEqual(result.max_probability, 0.8487, places=4)
def test_readme_sample(self):
""" readme sample test """
# pylint: disable=import-outside-toplevel,redefined-builtin
def print(*args):
""" overloads print to log values """
if args:
self.log.debug(args[0], *args[1:])
# --- Exact copy of sample code ----------------------------------------
import numpy as np
from qiskit import BasicAer
from qiskit.algorithms import AmplitudeEstimation, EstimationProblem
from qiskit.circuit.library import NormalDistribution
from qiskit_finance.applications import FixedIncomeExpectedValue
# Create a suitable multivariate distribution
num_qubits = [2, 2]
bounds = [(0, 0.12), (0, 0.24)]
mvnd = NormalDistribution(num_qubits,
mu=[0.12, 0.24],
sigma=0.01 * np.eye(2),
bounds=bounds)
# Create fixed income component
fixed_income = FixedIncomeExpectedValue(num_qubits,
np.eye(2),
np.zeros(2),
cash_flow=[1.0, 2.0],
rescaling_factor=0.125,
bounds=bounds)
# the FixedIncomeExpectedValue provides us with the necessary rescalings
# create the A operator for amplitude estimation by prepending the
# normal distribution to the function mapping
state_preparation = fixed_income.compose(mvnd, front=True)
problem = EstimationProblem(
state_preparation=state_preparation,
objective_qubits=[4],
post_processing=fixed_income.post_processing)
# Set number of evaluation qubits (samples)
num_eval_qubits = 5
# Construct and run amplitude estimation
q_i = BasicAer.get_backend('statevector_simulator')
algo = AmplitudeEstimation(num_eval_qubits=num_eval_qubits,
quantum_instance=q_i)
result = algo.estimate(problem)
print('Estimated value:\t%.4f' % result.estimation_processed)
print('Probability: \t%.4f' % result.max_probability)
# ----------------------------------------------------------------------
with self.subTest('test estimation'):
self.assertAlmostEqual(result.estimation_processed, 2.46, places=4)
with self.subTest('test max.probability'):
self.assertAlmostEqual(result.max_probability, 0.8487, places=4)