def test_recursive_warm_qaoa(self):
"""Test the recursive optimizer with warm start qaoa."""
algorithm_globals.random_seed = 12345
backend = BasicAer.get_backend("statevector_simulator")
qaoa = QAOA(quantum_instance=backend, reps=1)
warm_qaoa = WarmStartQAOAOptimizer(pre_solver=SlsqpOptimizer(),
relax_for_pre_solver=True,
qaoa=qaoa)
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(
warm_qaoa, min_num_vars=4)
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path("op_ip1.lp", "algorithms/resources")
problem.read_from_lp_file(lp_file)
# solve problem with cplex
cplex = CplexOptimizer(cplex_parameters={
"threads": 1,
"randomseed": 1
})
cplex_result = cplex.solve(problem)
# solve problem
result = recursive_min_eigen_optimizer.solve(problem)
# analyze results
np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
self.assertAlmostEqual(cplex_result.fval, result.fval)
def test_recursive_min_eigen_optimizer(self):
"""Test the recursive minimum eigen optimizer."""
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(filename, "algorithms/resources")
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
np.testing.assert_array_almost_equal(cplex_result.x, result.x, 4)
self.assertAlmostEqual(cplex_result.fval, result.fval)
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(filename, 'algorithms/resources')
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_recursive_history(self):
"""Tests different options for history."""
filename = "op_ip1.lp"
# load optimization problem
problem = QuadraticProgram()
lp_file = self.get_resource_path(filename, "algorithms/resources")
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])
def test_converter_list(self):
"""Test converter list"""
op = QuadraticProgram()
op.integer_var(0, 3, "x")
op.binary_var('y')
op.maximize(linear={'x': 1, 'y': 2})
op.linear_constraint(linear={'y': 1, 'x': 1}, sense='LE', rhs=3, name='xy_leq')
# construct minimum eigen optimizer
min_eigen_solver = NumPyMinimumEigensolver()
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver)
# a single converter
qp2qubo = QuadraticProgramToQubo()
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=2,
converters=qp2qubo)
result = recursive_min_eigen_optimizer.solve(op)
self.assertEqual(result.fval, 4)
# a list of converters
ineq2eq = InequalityToEquality()
int2bin = IntegerToBinary()
penalize = LinearEqualityToPenalty()
converters = [ineq2eq, int2bin, penalize]
recursive_min_eigen_optimizer = RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=2,
converters=converters)
result = recursive_min_eigen_optimizer.solve(op)
self.assertEqual(result.fval, 4)
# invalid converters
with self.assertRaises(TypeError):
invalid = [qp2qubo, "invalid converter"]
RecursiveMinimumEigenOptimizer(min_eigen_optimizer,
min_num_vars=2,
converters=invalid)