def test_docplex_tsp(self):
""" Docplex tsp test """
# Generating a graph of 3 nodes
n = 3
ins = tsp.random_tsp(n)
graph = rx.PyGraph()
graph.add_nodes_from(np.arange(0, n, 1).tolist())
num_node = ins.dim
# Create an Ising Hamiltonian with docplex.
mdl = Model(name='tsp')
x = {(i, p): mdl.binary_var(name='x_{0}_{1}'.format(i, p))
for i in range(num_node) for p in range(num_node)}
tsp_func = mdl.sum(
ins.w[i, j] * x[(i, p)] * x[(j, (p + 1) % num_node)]
for i in range(num_node) for j in range(num_node) for p
in range(num_node))
mdl.minimize(tsp_func)
for i in range(num_node):
mdl.add_constraint(mdl.sum(x[(i, p)] for p in range(num_node)) == 1)
for j in range(num_node):
mdl.add_constraint(mdl.sum(x[(i, j)] for i in range(num_node)) == 1)
qubit_op, offset = docplex.get_operator(mdl)
e_e = NumPyMinimumEigensolver()
result = e_e.compute_minimum_eigenvalue(operator=qubit_op)
ee_expected = NumPyMinimumEigensolver()
expected_result = ee_expected.compute_minimum_eigenvalue(operator=QUBIT_OP_TSP)
# Compare objective
self.assertAlmostEqual(result.eigenvalue.real + offset,
expected_result.eigenvalue.real + OFFSET_TSP)
def test_cme_aux_ops_dict(self):
"""Test dictionary compatibility of aux_operators"""
# Start with an empty dictionary
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators={})
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
# Add aux_operators dictionary and go again
result = algo.compute_minimum_eigenvalue(
operator=self.qubit_op, aux_operators=self.aux_ops_dict)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues["aux_op1"], [2, 0])
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues["aux_op2"], [0, 0])
# Add None and zero operators and go again
extra_ops = {"None_op": None, "zero_op": 0, **self.aux_ops_dict}
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=extra_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 3)
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues["aux_op1"], [2, 0])
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues["aux_op2"], [0, 0])
self.assertEqual(result.aux_operator_eigenvalues["zero_op"], (0.0, 0))
def test_docplex_maxcut(self):
""" Docplex maxcut test """
# Generating a graph of 4 nodes
n = 4
graph = rx.PyGraph()
graph.add_nodes_from(np.arange(0, n, 1).tolist())
elist = [(0, 1, 1.0), (0, 2, 1.0), (0, 3, 1.0), (1, 2, 1.0), (2, 3, 1.0)]
graph.add_edges_from(elist)
# Computing the weight matrix from the random graph
w = rx.graph_adjacency_matrix(graph, lambda weight: weight)
# Create an Ising Hamiltonian with docplex.
mdl = Model(name='max_cut')
mdl.node_vars = mdl.binary_var_list(list(range(4)), name='node')
maxcut_func = mdl.sum(w[i, j] * mdl.node_vars[i] * (1 - mdl.node_vars[j])
for i in range(n) for j in range(n))
mdl.maximize(maxcut_func)
qubit_op, offset = docplex.get_operator(mdl)
e_e = NumPyMinimumEigensolver()
result = e_e.compute_minimum_eigenvalue(operator=qubit_op)
ee_expected = NumPyMinimumEigensolver()
expected_result = ee_expected.compute_minimum_eigenvalue(operator=QUBIT_OP_MAXCUT)
# Compare objective
self.assertAlmostEqual(result.eigenvalue.real + offset,
expected_result.eigenvalue.real + OFFSET_MAXCUT)
def test_cme_reuse(self):
""" Test reuse """
# Start with no operator or aux_operators, give via compute method
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
# Add aux_operators and go again
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=self.aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[1], [0, 0])
# "Remove" aux_operators and go again
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=[])
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
# Set aux_operators and go again
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=self.aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[1], [0, 0])
# Finally just set one of aux_operators and main operator, remove aux_operators
result = algo.compute_minimum_eigenvalue(operator=self.aux_ops[0],
aux_operators=[])
self.assertAlmostEqual(result.eigenvalue, 2 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
def test_aux_operators_dict(self):
"""Test dict-based aux_operators."""
aux_op1 = PauliSumOp.from_list([("II", 2.0)])
aux_op2 = PauliSumOp.from_list([("II", 0.5), ("ZZ", 0.5), ("YY", 0.5),
("XX", -0.5)])
aux_ops = {"aux_op1": aux_op1, "aux_op2": aux_op2}
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
# expectation values
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op1"][0],
2,
places=6)
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op2"][0],
0,
places=6)
# standard deviations
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op1"][1],
0.0)
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op2"][1],
0.0)
# Go again with additional None and zero operators
extra_ops = {**aux_ops, "None_operator": None, "zero_operator": 0}
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=extra_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 3)
# expectation values
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op1"][0],
2,
places=6)
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op2"][0],
0,
places=6)
self.assertEqual(result.aux_operator_eigenvalues["zero_operator"][0],
0.0)
self.assertTrue(
"None_operator" not in result.aux_operator_eigenvalues.keys())
# standard deviations
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op1"][1],
0.0)
self.assertAlmostEqual(result.aux_operator_eigenvalues["aux_op2"][1],
0.0)
self.assertAlmostEqual(
result.aux_operator_eigenvalues["zero_operator"][1], 0.0)
def test_docplex_constant_and_quadratic_terms_in_object_function(self):
""" Docplex Constant and Quadratic terms in Object function test """
# Create an Ising Hamiltonian with docplex
laplacian = np.array([[-3., 1., 1., 1.],
[1., -2., 1., -0.],
[1., 1., -3., 1.],
[1., -0., 1., -2.]])
mdl = Model()
# pylint: disable=unsubscriptable-object
n = laplacian.shape[0]
bias = [0] * 4
x = {i: mdl.binary_var(name='x_{0}'.format(i)) for i in range(n)}
couplers_func = mdl.sum(
2 * laplacian[i, j] * (2 * x[i] - 1) * (2 * x[j] - 1)
for i in range(n - 1) for j in range(i, n))
bias_func = mdl.sum(float(bias[i]) * x[i] for i in range(n))
ising_func = couplers_func + bias_func
mdl.minimize(ising_func)
qubit_op, offset = docplex.get_operator(mdl)
e_e = NumPyMinimumEigensolver()
result = e_e.compute_minimum_eigenvalue(operator=qubit_op)
expected_result = -22
# Compare objective
self.assertAlmostEqual(result.eigenvalue.real + offset, expected_result)
def test_partition(self):
""" Partition test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op, aux_operators=[])
x = sample_most_likely(result.eigenstate)
if x[0] != 0:
x = np.logical_not(x) * 1
np.testing.assert_array_equal(x, [0, 1, 0])
def test_cme(self):
""" Basic test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=self.aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[1], [0, 0])
def test_tsp(self):
""" TSP test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op)
x = sample_most_likely(result.eigenstate)
# print(self.qubit_op.to_opflow().eval(result.eigenstate).adjoint().eval(result.eigenstate))
order = tsp.get_tsp_solution(x)
np.testing.assert_equal(tsp.tsp_value(order, self.ins.w),
tsp.tsp_value([1, 2, 0], self.ins.w))
def test_clique(self):
""" Clique test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op, aux_operators=[])
x = sample_most_likely(result.eigenstate)
ising_sol = clique.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 1, 1, 1])
oracle = self._brute_force()
self.assertEqual(clique.satisfy_or_not(ising_sol, self.w, self.k), oracle)
def test_portfolio(self):
""" portfolio test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op)
selection = OptimizationApplication.sample_most_likely(
result.eigenstate)
value = portfolio.portfolio_value(selection, self.muu, self.sigma,
self.risk, self.budget, self.penalty)
np.testing.assert_array_equal(selection, [0, 1, 1, 0])
self.assertAlmostEqual(value, -0.00679917)
def test_exact_cover(self):
""" Exact Cover test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op, aux_operators=[])
x = sample_most_likely(result.eigenstate)
ising_sol = exact_cover.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1, 0])
oracle = self._brute_force()
self.assertEqual(exact_cover.check_solution_satisfiability(ising_sol, self.list_of_subsets),
oracle)
def test_vertex_cover(self):
""" Vertex Cover test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=[])
x = sample_most_likely(result.eigenstate)
sol = vertex_cover.get_graph_solution(x)
np.testing.assert_array_equal(sol, [0, 0, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(sol), oracle)
def test_set_packing(self):
""" set packing test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=[])
x = sample_most_likely(result.eigenstate)
ising_sol = set_packing.get_solution(x)
np.testing.assert_array_equal(ising_sol, [0, 1, 1])
oracle = self._brute_force()
self.assertEqual(np.count_nonzero(ising_sol), oracle)
def _run_knapsack(values, weights, max_weight):
qubit_op, _ = knapsack.get_operator(values, weights, max_weight)
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=qubit_op)
x = sample_most_likely(result.eigenstate)
solution = knapsack.get_solution(x, values)
value, weight = knapsack.knapsack_value_weight(solution, values, weights)
return solution, value, weight
def test_stable_set(self):
""" Stable set test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(self.qubit_op,
aux_operators=[])
x = sample_most_likely(result.eigenstate)
self.assertAlmostEqual(result.eigenvalue.real, -39.5)
self.assertAlmostEqual(result.eigenvalue.real + self.offset, -38.0)
ising_sol = stable_set.get_graph_solution(x)
np.testing.assert_array_equal(ising_sol, [1, 1, 0, 1, 1])
self.assertEqual(stable_set.stable_set_value(x, self.w), (4, False))
def test_aux_operators_list(self):
"""Test list-based aux_operators."""
aux_op1 = PauliSumOp.from_list([("II", 2.0)])
aux_op2 = PauliSumOp.from_list([("II", 0.5), ("ZZ", 0.5), ("YY", 0.5),
("XX", -0.5)])
aux_ops = [aux_op1, aux_op2]
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
# expectation values
self.assertAlmostEqual(result.aux_operator_eigenvalues[0][0],
2,
places=6)
self.assertAlmostEqual(result.aux_operator_eigenvalues[1][0],
0,
places=6)
# standard deviations
self.assertAlmostEqual(result.aux_operator_eigenvalues[0][1], 0.0)
self.assertAlmostEqual(result.aux_operator_eigenvalues[1][1], 0.0)
# Go again with additional None and zero operators
extra_ops = [*aux_ops, None, 0]
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=extra_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 4)
# expectation values
self.assertAlmostEqual(result.aux_operator_eigenvalues[0][0],
2,
places=6)
self.assertAlmostEqual(result.aux_operator_eigenvalues[1][0],
0,
places=6)
self.assertIsNone(result.aux_operator_eigenvalues[2], None)
self.assertEqual(result.aux_operator_eigenvalues[3][0], 0.0)
# standard deviations
self.assertAlmostEqual(result.aux_operator_eigenvalues[0][1], 0.0)
self.assertAlmostEqual(result.aux_operator_eigenvalues[1][1], 0.0)
self.assertEqual(result.aux_operator_eigenvalues[3][1], 0.0)
def test_cme_filter_empty(self):
""" Test with filter always returning False """
# define filter criterion
# pylint: disable=unused-argument
def criterion(x, v, a_v):
return False
algo = NumPyMinimumEigensolver(filter_criterion=criterion)
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=self.aux_ops)
self.assertEqual(result.eigenvalue, None)
self.assertEqual(result.eigenstate, None)
self.assertEqual(result.aux_operator_eigenvalues, None)
def test_graph_partition(self):
""" Graph Partition test """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=[])
x = sample_most_likely(result.eigenstate)
# check against the oracle
ising_sol = graph_partition.get_graph_solution(x)
# solutions are equivalent
self.assertEqual(
graph_partition.objective_value(np.array([0, 1, 0, 1]), self.w),
graph_partition.objective_value(ising_sol, self.w))
oracle = self._brute_force()
self.assertEqual(graph_partition.objective_value(x, self.w), oracle)
def test_cme_filter(self):
""" Basic test """
# define filter criterion
# pylint: disable=unused-argument
def criterion(x, v, a_v):
return v >= -0.5
algo = NumPyMinimumEigensolver(filter_criterion=criterion)
result = algo.compute_minimum_eigenvalue(operator=self.qubit_op,
aux_operators=self.aux_ops)
self.assertAlmostEqual(result.eigenvalue, -0.22491125 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(result.aux_operator_eigenvalues[1], [0, 0])
def test_docplex_integer_constraints(self):
""" Docplex Integer Constraints test """
# Create an Ising Hamiltonian with docplex
mdl = Model(name='integer_constraints')
x = {i: mdl.binary_var(name='x_{0}'.format(i)) for i in range(1, 5)}
max_vars_func = mdl.sum(x[i] for i in range(1, 5))
mdl.maximize(max_vars_func)
mdl.add_constraint(mdl.sum(i * x[i] for i in range(1, 5)) == 3)
qubit_op, offset = docplex.get_operator(mdl)
e_e = NumPyMinimumEigensolver()
result = e_e.compute_minimum_eigenvalue(operator=qubit_op)
expected_result = -2
# Compare objective
self.assertAlmostEqual(result.eigenvalue.real + offset, expected_result)
def test_constants_in_left_side_and_variables_in_right_side(self):
""" Test Constant values on the left-hand side of constraints and
variables on the right-hand side of constraints for the DOcplex translator"""
mdl = Model('left_constants_and_right_variables')
x = mdl.binary_var(name='x')
y = mdl.binary_var(name='y')
mdl.maximize(mdl.sum(x + y))
mdl.add_constraint(x == y)
qubit_op, offset = docplex.get_operator(mdl)
self.log.debug(qubit_op)
e_e = NumPyMinimumEigensolver()
result = e_e.compute_minimum_eigenvalue(operator=qubit_op)
self.assertEqual(result.eigenvalue.real + offset, -2)
actual_sol = result.eigenstate.to_matrix().tolist()
self.assertListEqual(actual_sol, [0, 0, 0, 1])
def test_cme_reuse(self):
""" Test reuse """
# Start with no operator or aux_operators, give via compute method
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(self.qubit_op)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(self.qubit_op.to_opflow(), algo.operator)
self.assertIsNone(result.aux_operator_eigenvalues)
# Set operator to None and go again
algo.operator = None
with self.assertRaises(AlgorithmError):
_ = algo.run()
# Set operator back as it was and go again
algo.operator = self.qubit_op
result = algo.compute_minimum_eigenvalue()
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
# Add aux_operators and go again
result = algo.compute_minimum_eigenvalue(aux_operators=self.aux_ops)
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues[1], [0, 0])
# "Remove" aux_operators and go again
result = algo.compute_minimum_eigenvalue(aux_operators=[])
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
# Set aux_operators and go again
algo.aux_operators = self.aux_ops
result = algo.compute_minimum_eigenvalue()
self.assertAlmostEqual(result.eigenvalue, -1.85727503 + 0j)
self.assertEqual(len(result.aux_operator_eigenvalues), 2)
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues[0], [2, 0])
np.testing.assert_array_almost_equal(
result.aux_operator_eigenvalues[1], [0, 0])
# Finally just set one of aux_operators and main operator, remove aux_operators
result = algo.compute_minimum_eigenvalue(self.aux_ops[0], [])
self.assertAlmostEqual(result.eigenvalue, 2 + 0j)
self.assertIsNone(result.aux_operator_eigenvalues)
def test_cme_1q(self, op):
""" Test for 1 qubit operator """
algo = NumPyMinimumEigensolver()
result = algo.compute_minimum_eigenvalue(operator=op)
self.assertAlmostEqual(result.eigenvalue, -1)