def test_simple2(self):
""" simple2 test """
# Computes the cost using the exact eigensolver
# and compares it against pre-determined value.
result = NumPyMinimumEigensolver(self.qubit_op).run()
quantum_solution = get_portfoliodiversification_solution(
self.instance, self.n, self.q, result)
ground_level = get_portfoliodiversification_value(
self.instance, self.n, self.q, quantum_solution)
np.testing.assert_approx_equal(ground_level, 1.8)
def test_portfolio_diversification(self):
""" portfolio diversification test """
# Something of an integration test
# Solve the problem in a classical fashion via CPLEX and compare the solution
# Note that CPLEX uses a completely different integer linear programming formulation.
if not _HAS_CPLEX:
self.skipTest('CPLEX is not installed.')
x = None
classical_optimizer = ClassicalOptimizer(self.instance, self.n, self.q)
x, classical_cost = classical_optimizer.cplex_solution()
# Solve the problem using the exact eigensolver
result = NumPyMinimumEigensolver(self.qubit_op).run()
quantum_solution = get_portfoliodiversification_solution(
self.instance, self.n, self.q, result)
ground_level = get_portfoliodiversification_value(
self.instance, self.n, self.q, quantum_solution)
if x is not None:
np.testing.assert_approx_equal(ground_level, classical_cost)
np.testing.assert_array_almost_equal(quantum_solution, x, 5)
def test_portfolio_diversification(self):
""" portfolio diversification test """
# Something of an integration test
# Solve the problem in a classical fashion via CPLEX and compare the solution
# Note that CPLEX uses a completely different integer linear programming formulation.
x = None
try:
classical_optimizer = ClassicalOptimizer(self.instance, self.n, self.q)
x, classical_cost = classical_optimizer.cplex_solution()
except Exception: # pylint: disable=broad-except
# This test should not focus on the availability of CPLEX, so we just eat the exception.
self.skipTest("CPLEX may be missing.")
# Solve the problem using the exact eigensolver
result = NumPyMinimumEigensolver(self.qubit_op).run()
quantum_solution = get_portfoliodiversification_solution(self.instance,
self.n,
self.q, result)
ground_level = get_portfoliodiversification_value(self.instance,
self.n,
self.q, quantum_solution)
if x is not None:
np.testing.assert_approx_equal(ground_level, classical_cost)
np.testing.assert_array_almost_equal(quantum_solution, x, 5)
