def test_integer_to_binary2(self):
"""Test integer to binary variables 2"""
mod = QuadraticProgram()
mod.integer_var(name='x', lowerbound=0, upperbound=1)
mod.integer_var(name='y', lowerbound=0, upperbound=1)
mod.minimize(1, {'x': 1}, {('x', 'y'): 2})
mod.linear_constraint({'x': 1}, '==', 1)
mod.quadratic_constraint({'x': 1}, {('x', 'y'): 2}, '==', 1)
mod2 = IntegerToBinary().convert(mod)
self.assertListEqual([e.name + '@0' for e in mod.variables],
[e.name for e in mod2.variables])
self.assertDictEqual(mod.objective.linear.to_dict(),
mod2.objective.linear.to_dict())
self.assertDictEqual(mod.objective.quadratic.to_dict(),
mod2.objective.quadratic.to_dict())
self.assertEqual(mod.get_num_linear_constraints(),
mod2.get_num_linear_constraints())
for cst, cst2 in zip(mod.linear_constraints, mod2.linear_constraints):
self.assertDictEqual(cst.linear.to_dict(), cst2.linear.to_dict())
self.assertEqual(mod.get_num_quadratic_constraints(),
mod2.get_num_quadratic_constraints())
for cst, cst2 in zip(mod.quadratic_constraints,
mod2.quadratic_constraints):
self.assertDictEqual(cst.linear.to_dict(), cst2.linear.to_dict())
self.assertDictEqual(cst.quadratic.to_dict(),
cst2.quadratic.to_dict())
def test_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {'x0': 1, 'x1': 2, 'x2': 1}
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
op2 = conv.convert(op)
result = OptimizationResult(x=[0, 1, 1, 1, 1],
fval=17,
variables=op2.variables)
new_result = conv.interpret(result)
np.testing.assert_array_almost_equal(new_result.x, [0, 1, 5])
self.assertEqual(new_result.fval, 17)
self.assertListEqual(new_result.variable_names, ['x0', 'x1', 'x2'])
self.assertDictEqual(new_result.variables_dict, {
'x0': 0,
'x1': 1,
'x2': 5
})
def test_continuous_variable_decode(self):
""" Test decode func of IntegerToBinaryConverter for continuous variables"""
try:
mdl = Model('test_continuous_varable_decode')
c = mdl.continuous_var(lb=0, ub=10.9, name='c')
x = mdl.binary_var(name='x')
mdl.maximize(c + x * x)
op = QuadraticProgram()
op.from_docplex(mdl)
converter = IntegerToBinary()
op = converter.convert(op)
admm_params = ADMMParameters()
qubo_optimizer = MinimumEigenOptimizer(NumPyMinimumEigensolver())
continuous_optimizer = CplexOptimizer()
solver = ADMMOptimizer(
qubo_optimizer=qubo_optimizer,
continuous_optimizer=continuous_optimizer,
params=admm_params,
)
result = solver.solve(op)
result = converter.interpret(result)
self.assertEqual(result.x[0], 10.9)
self.assertListEqual(result.variable_names, ['c', 'x'])
self.assertDictEqual(result.variables_dict, {'c': 10.9, 'x': 0})
except NameError as ex:
self.skipTest(str(ex))
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)
self.assertEqual(result.fval, 4)
with self.assertRaises(TypeError):
invalid = [qp2qubo, "invalid converter"]
MinimumEigenOptimizer(min_eigen_solver, converters=invalid)
def test_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {'x0': 1, 'x1': 2, 'x2': 1}
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
_ = conv.convert(op)
new_x = conv.interpret([0, 1, 1, 1, 1])
np.testing.assert_array_almost_equal(new_x, [0, 1, 5])
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_integer_to_binary(self):
""" Test integer to binary """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.maximize(0, linear, {})
conv = IntegerToBinary()
op2 = conv.encode(op)
for x in op2.variables:
self.assertEqual(x.vartype, Variable.Type.BINARY)
dct = op2.objective.linear.to_dict()
self.assertEqual(dct[2], 3)
self.assertEqual(dct[3], 6)
self.assertEqual(dct[4], 6)
def test_integer_to_binary(self):
""" Test integer to binary """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.maximize(0, linear, {})
conv = IntegerToBinary()
op2 = conv.convert(op)
self.assertEqual(op2.get_num_vars(), 5)
self.assertListEqual([x.vartype for x in op2.variables], [Variable.Type.BINARY] * 5)
self.assertListEqual([x.name for x in op2.variables], ['x0', 'x1', 'x2@0', 'x2@1', 'x2@2'])
dct = op2.objective.linear.to_dict()
self.assertEqual(dct[2], 3)
self.assertEqual(dct[3], 6)
self.assertEqual(dct[4], 6)
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_binary_to_integer(self):
""" Test binary to integer """
op = QuadraticProgram()
for i in range(0, 2):
op.binary_var(name='x{}'.format(i))
op.integer_var(name='x2', lowerbound=0, upperbound=5)
linear = {}
linear['x0'] = 1
linear['x1'] = 2
linear['x2'] = 1
op.maximize(0, linear, {})
linear = {}
for x in op.variables:
linear[x.name] = 1
op.linear_constraint(linear, Constraint.Sense.EQ, 6, 'x0x1x2')
conv = IntegerToBinary()
_ = conv.encode(op)
result = OptimizationResult(x=[0, 1, 1, 1, 1], fval=17)
new_result = conv.decode(result)
self.assertListEqual(new_result.x, [0, 1, 5])
self.assertEqual(new_result.fval, 17)
def test_empty_problem_deprecated(self):
""" Test empty problem """
op = QuadraticProgram()
conv = InequalityToEquality()
op = conv.encode(op)
conv = IntegerToBinary()
op = conv.encode(op)
conv = LinearEqualityToPenalty()
op = conv.encode(op)
conv = QuadraticProgramToIsing()
_, shift = conv.encode(op)
self.assertEqual(shift, 0.0)
def test_continuous_variable_decode(self):
""" Test decode func of IntegerToBinaryConverter for continuous variables"""
try:
mdl = Model('test_continuous_varable_decode')
c = mdl.continuous_var(lb=0, ub=10.9, name='c')
x = mdl.binary_var(name='x')
mdl.maximize(c + x * x)
op = QuadraticProgram()
op.from_docplex(mdl)
converter = IntegerToBinary()
op = converter.encode(op)
admm_params = ADMMParameters()
qubo_optimizer = MinimumEigenOptimizer(NumPyMinimumEigensolver())
continuous_optimizer = CplexOptimizer()
solver = ADMMOptimizer(
qubo_optimizer=qubo_optimizer,
continuous_optimizer=continuous_optimizer,
params=admm_params,
)
solution = solver.solve(op)
solution = converter.decode(solution)
self.assertEqual(solution.x[0], 10.9)
except NameError as ex:
self.skipTest(str(ex))
def test_empty_problem_deprecated(self):
""" Test empty problem """
try:
warnings.filterwarnings(action="ignore",
category=DeprecationWarning)
op = QuadraticProgram()
conv = InequalityToEquality()
op = conv.encode(op)
conv = IntegerToBinary()
op = conv.encode(op)
conv = LinearEqualityToPenalty()
op = conv.encode(op)
conv = QuadraticProgramToIsing()
_, shift = conv.encode(op)
finally:
warnings.filterwarnings(action="always",
category=DeprecationWarning)
self.assertEqual(shift, 0.0)
