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)