def test_ecev(self):
""" European Call Expected Value test """
bounds = np.array([0., 7.])
num_qubits = [3]
entangler_map = []
for i in range(sum(num_qubits)):
entangler_map.append([i, int(np.mod(i + 1, sum(num_qubits)))])
g_params = [
0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428,
0.13537225
]
# Set an initial state for the generator circuit
init_dist = NormalDistribution(int(sum(num_qubits)),
mu=1.,
sigma=1.,
low=bounds[0],
high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits),
state_vector=init_distribution)
var_form = TwoLocal(int(np.sum(num_qubits)),
'ry',
'cz',
reps=1,
initial_state=init_distribution,
entanglement=entangler_map)
uncertainty_model = UnivariateVariationalDistribution(int(
sum(num_qubits)),
var_form,
g_params,
low=bounds[0],
high=bounds[1])
strike_price = 2
c_approx = 0.25
european_call = EuropeanCallExpectedValue(uncertainty_model,
strike_price=strike_price,
c_approx=c_approx)
uncertainty_model.set_probabilities(
QuantumInstance(BasicAer.get_backend('statevector_simulator')))
algo = AmplitudeEstimation(5, european_call)
result = algo.run(
quantum_instance=BasicAer.get_backend('statevector_simulator'))
self.assertAlmostEqual(result['estimation'], 1.2580, places=4)
self.assertAlmostEqual(result['max_probability'], 0.8785, places=4)
def test_ecev(self, use_circuits):
""" European Call Expected Value test """
if not use_circuits:
# ignore deprecation warnings from the deprecation of VariationalForm as input for
# the univariate variational distribution
warnings.filterwarnings("ignore", category=DeprecationWarning)
bounds = np.array([0., 7.])
num_qubits = [3]
entangler_map = []
for i in range(sum(num_qubits)):
entangler_map.append([i, int(np.mod(i + 1, sum(num_qubits)))])
g_params = [
0.29399714, 0.38853322, 0.9557694, 0.07245791, 6.02626428,
0.13537225
]
# Set an initial state for the generator circuit
init_dist = NormalDistribution(int(sum(num_qubits)),
mu=1.,
sigma=1.,
low=bounds[0],
high=bounds[1])
init_distribution = np.sqrt(init_dist.probabilities)
init_distribution = Custom(num_qubits=sum(num_qubits),
state_vector=init_distribution)
var_form = RY(int(np.sum(num_qubits)),
depth=1,
initial_state=init_distribution,
entangler_map=entangler_map,
entanglement_gate='cz')
if use_circuits:
theta = ParameterVector('θ', var_form.num_parameters)
var_form = var_form.construct_circuit(theta)
uncertainty_model = UnivariateVariationalDistribution(int(
sum(num_qubits)),
var_form,
g_params,
low=bounds[0],
high=bounds[1])
if use_circuits:
uncertainty_model._var_form_params = theta
strike_price = 2
c_approx = 0.25
european_call = EuropeanCallExpectedValue(uncertainty_model,
strike_price=strike_price,
c_approx=c_approx)
uncertainty_model.set_probabilities(
QuantumInstance(BasicAer.get_backend('statevector_simulator')))
algo = AmplitudeEstimation(5, european_call)
result = algo.run(
quantum_instance=BasicAer.get_backend('statevector_simulator'))
self.assertAlmostEqual(result['estimation'], 1.2580, places=4)
self.assertAlmostEqual(result['max_probability'], 0.8785, places=4)
if not use_circuits:
warnings.filterwarnings(action="always",
category=DeprecationWarning)