def test_converter_list(self):
"""Test converters list"""
# Input.
model = Model()
x_0 = model.binary_var(name='x0')
x_1 = model.binary_var(name='x1')
model.maximize(-x_0+2*x_1)
op = QuadraticProgram()
op.from_docplex(model)
# Get the optimum key and value.
n_iter = 8
# a single converter.
qp2qubo = QuadraticProgramToQubo()
gmf = GroverOptimizer(4, num_iterations=n_iter, quantum_instance=self.sv_simulator,
converters=qp2qubo)
results = gmf.solve(op)
self.validate_results(op, results)
# a list of converters
ineq2eq = InequalityToEquality()
int2bin = IntegerToBinary()
penalize = LinearEqualityToPenalty()
converters = [ineq2eq, int2bin, penalize]
gmf = GroverOptimizer(4, num_iterations=n_iter, quantum_instance=self.sv_simulator,
converters=converters)
results = gmf.solve(op)
self.validate_results(op, results)
# invalid converters
with self.assertRaises(TypeError):
invalid = [qp2qubo, "invalid converter"]
GroverOptimizer(4, num_iterations=n_iter,
quantum_instance=self.sv_simulator,
converters=invalid)
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={'x': 1, 'y': 1}, sense='LE', rhs=3, name='xy_leq')
min_eigen_solver = NumPyMinimumEigensolver()
# a single converter
qp2qubo = QuadraticProgramToQubo()
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver, converters=qp2qubo)
result = min_eigen_optimizer.solve(op)
self.assertEqual(result.fval, 4)
# a list of converters
ineq2eq = InequalityToEquality()
int2bin = IntegerToBinary()
penalize = LinearEqualityToPenalty()
converters = [ineq2eq, int2bin, penalize]
min_eigen_optimizer = MinimumEigenOptimizer(min_eigen_solver, converters=converters)
result = min_eigen_optimizer.solve(op)
self.assertEqual(result.fval, 4)
with self.assertRaises(TypeError):
invalid = [qp2qubo, "invalid converter"]
MinimumEigenOptimizer(min_eigen_solver,
converters=invalid)
def test_empty_problem(self):
""" Test empty problem """
op = QuadraticProgram()
conv = InequalityToEquality()
op = conv.convert(op)
conv = IntegerToBinary()
op = conv.convert(op)
conv = LinearEqualityToPenalty()
op = conv.convert(op)
_, shift = op.to_ising()
self.assertEqual(shift, 0.0)
def test_0var_range_inequality(self):
""" Test InequalityToEquality converter when the var_rang of the slack variable is 0"""
op = QuadraticProgram()
op.binary_var('x')
op.binary_var('y')
op.linear_constraint(linear={'x': 1, 'y': 1}, sense='LE', rhs=0, name='xy_leq1')
op.linear_constraint(linear={'x': 1, 'y': 1}, sense='GE', rhs=2, name='xy_geq1')
op.quadratic_constraint(quadratic={('x', 'x'): 1}, sense='LE', rhs=0, name='xy_leq2')
op.quadratic_constraint(quadratic={('x', 'y'): 1}, sense='GE', rhs=1, name='xy_geq2')
ineq2eq = InequalityToEquality()
new_op = ineq2eq.convert(op)
self.assertEqual(new_op.get_num_vars(), 2)
self.assertTrue(all(l_const.sense == Constraint.Sense.EQ
for l_const in new_op.linear_constraints))
self.assertTrue(all(q_const.sense == Constraint.Sense.EQ
for q_const in new_op.quadratic_constraints))
def test_inequality_mode_auto(self):
""" Test auto mode of InequalityToEqualityConverter() """
op = QuadraticProgram()
for i in range(3):
op.binary_var(name='x{}'.format(i))
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 2, 'x1x2')
linear_constraint = {'x0': 1.1, 'x2': 2.2}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 3.3, 'x0x2')
conv = InequalityToEquality(mode='auto')
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(lst, [Variable.Type.INTEGER, Variable.Type.CONTINUOUS])
def test_inequality_mode_continuous(self):
"""Test continuous mode of InequalityToEqualityConverter()"""
op = QuadraticProgram()
for i in range(3):
op.binary_var(name="x{}".format(i))
# Linear constraints
linear_constraint = {"x0": 1, "x1": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, "x0x1")
linear_constraint = {"x1": 1, "x2": -1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
conv = InequalityToEquality(mode="continuous")
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(
lst, [Variable.Type.CONTINUOUS, Variable.Type.CONTINUOUS])
def test_inequality_mode_integer(self):
"""Test integer mode of InequalityToEqualityConverter()"""
op = QuadraticProgram()
for i in range(3):
op.binary_var(name=f"x{i}")
# Linear constraints
linear_constraint = {"x0": 1, "x1": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, "x0x1")
linear_constraint = {"x1": 1, "x2": -1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
conv = InequalityToEquality(mode="integer")
op2 = conv.convert(op)
lst = [op2.variables[3].vartype, op2.variables[4].vartype]
self.assertListEqual(lst,
[Variable.Type.INTEGER, Variable.Type.INTEGER])
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)
def test_0var_range_inequality(self):
"""Test InequalityToEquality converter when the var_rang of the slack variable is 0"""
op = QuadraticProgram()
op.binary_var("x")
op.binary_var("y")
op.linear_constraint(linear={
"x": 1,
"y": 1
},
sense="LE",
rhs=0,
name="xy_leq1")
op.linear_constraint(linear={
"x": 1,
"y": 1
},
sense="GE",
rhs=2,
name="xy_geq1")
op.quadratic_constraint(quadratic={("x", "x"): 1},
sense="LE",
rhs=0,
name="xy_leq2")
op.quadratic_constraint(quadratic={("x", "y"): 1},
sense="GE",
rhs=1,
name="xy_geq2")
ineq2eq = InequalityToEquality()
new_op = ineq2eq.convert(op)
self.assertEqual(new_op.get_num_vars(), 2)
self.assertTrue(
all(l_const.sense == Constraint.Sense.EQ
for l_const in new_op.linear_constraints))
self.assertTrue(
all(q_const.sense == Constraint.Sense.EQ
for q_const in new_op.quadratic_constraints))
def test_inequality_binary(self):
"""Test InequalityToEqualityConverter with binary variables"""
op = QuadraticProgram()
for i in range(3):
op.binary_var(name="x{}".format(i))
# Linear constraints
linear_constraint = {"x0": 1, "x1": 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, "x0x1")
linear_constraint = {"x1": 1, "x2": -1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 2, "x1x2")
linear_constraint = {"x0": 1, "x2": 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, "x0x2")
# Quadratic constraints
quadratic = {("x0", "x1"): 1, ("x1", "x2"): 2}
op.quadratic_constraint({}, quadratic, Constraint.Sense.LE, 3,
"x0x1_x1x2LE")
quadratic = {("x0", "x1"): 3, ("x1", "x2"): 4}
op.quadratic_constraint({}, quadratic, Constraint.Sense.GE, 3,
"x0x1_x1x2GE")
# Convert inequality constraints into equality constraints
conv = InequalityToEquality()
op2 = conv.convert(op)
self.assertListEqual(
[v.name for v in op2.variables],
[
"x0",
"x1",
"x2",
"x1x2@int_slack",
"x0x2@int_slack",
"x0x1_x1x2LE@int_slack",
"x0x1_x1x2GE@int_slack",
],
)
# Check names and objective senses
self.assertEqual(op.name, op2.name)
self.assertEqual(op.objective.sense, op2.objective.sense)
# For linear constraints
lst = [
op2.linear_constraints[0].linear.to_dict()[0],
op2.linear_constraints[0].linear.to_dict()[1],
]
self.assertListEqual(lst, [1, 1])
self.assertEqual(op2.linear_constraints[0].sense, Constraint.Sense.EQ)
lst = [
op2.linear_constraints[1].linear.to_dict()[1],
op2.linear_constraints[1].linear.to_dict()[2],
op2.linear_constraints[1].linear.to_dict()[3],
]
self.assertListEqual(lst, [1, -1, 1])
lst = [op2.variables[3].lowerbound, op2.variables[3].upperbound]
self.assertListEqual(lst, [0, 3])
self.assertEqual(op2.linear_constraints[1].sense, Constraint.Sense.EQ)
lst = [
op2.linear_constraints[2].linear.to_dict()[0],
op2.linear_constraints[2].linear.to_dict()[2],
op2.linear_constraints[2].linear.to_dict()[4],
]
self.assertListEqual(lst, [1, 3, -1])
lst = [op2.variables[4].lowerbound, op2.variables[4].upperbound]
self.assertListEqual(lst, [0, 2])
self.assertEqual(op2.linear_constraints[2].sense, Constraint.Sense.EQ)
# For quadratic constraints
lst = [
op2.quadratic_constraints[0].quadratic.to_dict()[(0, 1)],
op2.quadratic_constraints[0].quadratic.to_dict()[(1, 2)],
op2.quadratic_constraints[0].linear.to_dict()[5],
]
self.assertListEqual(lst, [1, 2, 1])
lst = [op2.variables[5].lowerbound, op2.variables[5].upperbound]
self.assertListEqual(lst, [0, 3])
lst = [
op2.quadratic_constraints[1].quadratic.to_dict()[(0, 1)],
op2.quadratic_constraints[1].quadratic.to_dict()[(1, 2)],
op2.quadratic_constraints[1].linear.to_dict()[6],
]
self.assertListEqual(lst, [3, 4, -1])
lst = [op2.variables[6].lowerbound, op2.variables[6].upperbound]
self.assertListEqual(lst, [0, 4])
new_x = conv.interpret(np.arange(7))
np.testing.assert_array_almost_equal(new_x, np.arange(3))
def test_inequality_integer(self):
""" Test InequalityToEqualityConverter with integer variables """
op = QuadraticProgram()
for i in range(3):
op.integer_var(name='x{}'.format(i), lowerbound=-3, upperbound=3)
# Linear constraints
linear_constraint = {'x0': 1, 'x1': 1}
op.linear_constraint(linear_constraint, Constraint.Sense.EQ, 1, 'x0x1')
linear_constraint = {'x1': 1, 'x2': -1}
op.linear_constraint(linear_constraint, Constraint.Sense.LE, 2, 'x1x2')
linear_constraint = {'x0': 1, 'x2': 3}
op.linear_constraint(linear_constraint, Constraint.Sense.GE, 2, 'x0x2')
# Quadratic constraints
quadratic = {('x0', 'x1'): 1, ('x1', 'x2'): 2}
op.quadratic_constraint({}, quadratic, Constraint.Sense.LE, 3, 'x0x1_x1x2LE')
quadratic = {('x0', 'x1'): 3, ('x1', 'x2'): 4}
op.quadratic_constraint({}, quadratic, Constraint.Sense.GE, 3, 'x0x1_x1x2GE')
conv = InequalityToEquality()
op2 = conv.convert(op)
self.assertListEqual([v.name for v in op2.variables],
['x0', 'x1', 'x2', 'x1x2@int_slack', 'x0x2@int_slack',
'x0x1_x1x2LE@int_slack', 'x0x1_x1x2GE@int_slack'])
# For linear constraints
lst = [
op2.linear_constraints[0].linear.to_dict()[0],
op2.linear_constraints[0].linear.to_dict()[1],
]
self.assertListEqual(lst, [1, 1])
self.assertEqual(op2.linear_constraints[0].sense, Constraint.Sense.EQ)
lst = [
op2.linear_constraints[1].linear.to_dict()[1],
op2.linear_constraints[1].linear.to_dict()[2],
op2.linear_constraints[1].linear.to_dict()[3],
]
self.assertListEqual(lst, [1, -1, 1])
lst = [op2.variables[3].lowerbound, op2.variables[3].upperbound]
self.assertListEqual(lst, [0, 8])
self.assertEqual(op2.linear_constraints[1].sense, Constraint.Sense.EQ)
lst = [
op2.linear_constraints[2].linear.to_dict()[0],
op2.linear_constraints[2].linear.to_dict()[2],
op2.linear_constraints[2].linear.to_dict()[4],
]
self.assertListEqual(lst, [1, 3, -1])
lst = [op2.variables[4].lowerbound, op2.variables[4].upperbound]
self.assertListEqual(lst, [0, 10])
self.assertEqual(op2.linear_constraints[2].sense, Constraint.Sense.EQ)
# For quadratic constraints
lst = [
op2.quadratic_constraints[0].quadratic.to_dict()[(0, 1)],
op2.quadratic_constraints[0].quadratic.to_dict()[(1, 2)],
op2.quadratic_constraints[0].linear.to_dict()[5],
]
self.assertListEqual(lst, [1, 2, 1])
lst = [op2.variables[5].lowerbound, op2.variables[5].upperbound]
self.assertListEqual(lst, [0, 30])
lst = [
op2.quadratic_constraints[1].quadratic.to_dict()[(0, 1)],
op2.quadratic_constraints[1].quadratic.to_dict()[(1, 2)],
op2.quadratic_constraints[1].linear.to_dict()[6],
]
self.assertListEqual(lst, [3, 4, -1])
lst = [op2.variables[6].lowerbound, op2.variables[6].upperbound]
self.assertListEqual(lst, [0, 60])
new_x = conv.interpret(np.arange(7))
np.testing.assert_array_almost_equal(new_x, np.arange(3))