def test_min_eigen_optimizer(self, config):
""" Min Eigen Optimizer Test """
try:
# unpack configuration
min_eigen_solver_name, backend, filename = config
# get minimum eigen solver
min_eigen_solver = self.min_eigen_solvers[min_eigen_solver_name]
if backend:
min_eigen_solver.quantum_instance = BasicAer.get_backend(backend)
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# load optimization problem
problem = QuadraticProgram()
problem.read_from_lp_file(self.resource_path + filename)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = min_eigen_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
except RuntimeError as ex:
msg = str(ex)
if 'CPLEX' in msg:
self.skipTest(msg)
else:
self.fail(msg)
def test_recursive_min_eigen_optimizer(self):
"""Test the recursive minimum eigen optimizer."""
try:
filename = 'op_ip1.lp'
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
problem.read_from_lp_file(self.resource_path + filename)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
except RuntimeError as ex:
msg = str(ex)
if 'CPLEX' in msg:
self.skipTest(msg)
else:
self.fail(msg)
def test_recursive_min_eigen_optimizer(self):
"""Test the recursive minimum eigen optimizer."""
try:
filename = 'op_ip1.lp'
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_min_eigen_optimizer_history(self):
"""Tests different options for history."""
try:
filename = 'op_ip1.lp'
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# no history
recursive_min_eigen_optimizer = \
RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=4,
history=IntermediateResult.NO_ITERATIONS)
result = recursive_min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result.replacements)
self.assertIsNotNone(result.history)
self.assertIsNotNone(result.history[0])
self.assertEqual(len(result.history[0]), 0)
self.assertIsNone(result.history[1])
# only last iteration in the history
recursive_min_eigen_optimizer = \
RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=4,
history=IntermediateResult.LAST_ITERATION)
result = recursive_min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result.replacements)
self.assertIsNotNone(result.history)
self.assertIsNotNone(result.history[0])
self.assertEqual(len(result.history[0]), 0)
self.assertIsNotNone(result.history[1])
# full history
recursive_min_eigen_optimizer = \
RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=4,
history=IntermediateResult.ALL_ITERATIONS)
result = recursive_min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result.replacements)
self.assertIsNotNone(result.history)
self.assertIsNotNone(result.history[0])
self.assertGreater(len(result.history[0]), 1)
self.assertIsNotNone(result.history[1])
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_cplex_optimizer(self, config):
""" Cplex Optimizer Test """
# unpack configuration
filename, x, fval = config
# load optimization problem
problem = QuadraticProgram()
problem.read_from_lp_file(self.resource_path + filename)
# solve problem with cplex
result = self.cplex_optimizer.solve(problem)
# analyze results
self.assertAlmostEqual(result.fval, fval)
for i in range(problem.get_num_vars()):
self.assertAlmostEqual(result.x[i], x[i])
def test_min_eigen_optimizer_with_filter(self, config):
""" Min Eigen Optimizer Test """
try:
# unpack configuration
filename, lowerbound, fval, status = config
# get minimum eigen solver
min_eigen_solver = NumPyMinimumEigensolver()
# set filter
# pylint: disable=unused-argument
def filter_criterion(x, v, aux):
return v > lowerbound
min_eigen_solver.filter_criterion = filter_criterion
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# solve problem
result = min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result)
# analyze results
self.assertAlmostEqual(fval, result.fval)
self.assertEqual(status, result.status)
# check that eigensolver result is present
self.assertIsNotNone(result.min_eigen_solver_result)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
except RuntimeError as ex:
self.fail(str(ex))
def test_min_eigen_optimizer(self, config):
""" Min Eigen Optimizer Test """
try:
# unpack configuration
min_eigen_solver_name, backend, filename = config
# get minimum eigen solver
min_eigen_solver = self.min_eigen_solvers[min_eigen_solver_name]
if backend:
min_eigen_solver.quantum_instance = BasicAer.get_backend(
backend)
# construct minimum eigen optimizer
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(path.join('resources', filename))
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer()
cplex_result = cplex.solve(problem)
# solve problem
result = min_eigen_optimizer.solve(problem)
self.assertIsNotNone(result)
# analyze results
self.assertAlmostEqual(cplex_result.fval, result.fval)
# check that eigensolver result is present
self.assertIsNotNone(result.min_eigen_solver_result)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
except RuntimeError as ex:
self.fail(str(ex))
