def EstimatedVaR(x):
cdf_objective = FixedValueComparator(agg.num_sum_qubits, x + 1, geq=False)
multivariate_var = MultivariateProblem(u, agg, cdf_objective)
num_eval_qubits = 4
ae_var = AmplitudeEstimation(num_eval_qubits, multivariate_var)
result_var = ae_var.run(
quantum_instance=BasicAer.get_backend('statevector_simulator'))
#print("result_var['estimation']: ", result_var['estimation'])
return result_var['estimation']
def test_fixed_value_comparator(self, num_state_qubits, value, geq):
""" fixed value comparator test """
# initialize weighted sum operator factory
comp = Comparator(num_state_qubits, value, geq)
# initialize circuit
q = QuantumRegister(num_state_qubits + 1)
if comp.required_ancillas() > 0:
q_a = QuantumRegister(comp.required_ancillas())
qc = QuantumCircuit(q, q_a)
else:
q_a = None
qc = QuantumCircuit(q)
# set equal superposition state
qc.h(q[:num_state_qubits])
# build circuit
comp.build(qc, q, q_a)
# run simulation
job = execute(qc,
BasicAer.get_backend('statevector_simulator'),
shots=1)
for i, s_a in enumerate(job.result().get_statevector()):
prob = np.abs(s_a)**2
if prob > 1e-6:
# equal superposition
self.assertEqual(True,
np.isclose(1.0, prob * 2.0**num_state_qubits))
b_value = '{0:b}'.format(i).rjust(qc.width(), '0')
x = int(b_value[(-num_state_qubits):], 2)
comp_result = int(b_value[-num_state_qubits - 1], 2)
if geq:
self.assertEqual(x >= value, comp_result == 1)
else:
self.assertEqual(x < value, comp_result == 1)
# plt.xticks(size=15)
# plt.yticks([0, 0.25, 0.5, 0.75, 1], size=15)
# plt.title('Expected Loss', size=15)
# plt.ylabel('Probability', size=15)
# plt.ylim((0,1))
# plt.grid()
# plt.show()
#partie 3 estimation de CDF (cumulative distribution function) en utilisant QAE
print("----------estimaton de CDF-------------------------")
# fixer la valeur x pour estimer le CDF
x_eval = 2
# définition du circuit de comparaison
cdf_objective = FixedValueComparator(agg.num_sum_qubits, x_eval + 1, geq=False)
# définition du "Multivariate Problem"
multivariate_cdf = MultivariateProblem(u, agg, cdf_objective)
# obtenir le nombre de qubits nécessaires pour représenter le problème
num_qubits = multivariate_cdf.num_target_qubits
# obtenir le nombre de qubits auxiliaires nécessaires pour représenter le problème
num_ancillas = multivariate_cdf.required_ancillas()
# construire le circuit
q = QuantumRegister(num_qubits, name='q')
q_a = QuantumRegister(num_ancillas, name='q_a')
qc = QuantumCircuit(q, q_a)
multivariate_cdf.build(qc, q, q_a)