def test_optimizationproblem_to_ising(self):
""" Test optimization problem to operators"""
op = QuadraticProgram()
for i in range(4):
op.binary_var(name='x{}'.format(i))
linear = {}
for x in op.variables:
linear[x.name] = 1
op.maximize(0, linear, {})
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.linear_constraint(linear, Constraint.Sense.EQ, 3, 'sum1')
penalize = LinearEqualityToPenalty()
op2ope = QuadraticProgramToIsing()
op2 = penalize.encode(op)
qubitop, offset = op2ope.encode(op2)
# the encoder uses a dictionary, in which the order of items in Python 3.5 is not
# maintained, therefore don't do a list compare but dictionary compare
qubit_op_as_dict = dict(qubitop.paulis)
for coeff, paulis in QUBIT_OP_MAXIMIZE_SAMPLE.paulis:
self.assertEqual(paulis, qubit_op_as_dict[coeff])
self.assertEqual(offset, OFFSET_MAXIMIZE_SAMPLE)
def test_optimizationproblem_to_ising_deprecated(self):
""" Test optimization problem to operators"""
op = QuadraticProgram()
for i in range(4):
op.binary_var(name='x{}'.format(i))
linear = {}
for x in op.variables:
linear[x.name] = 1
op.maximize(0, linear, {})
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.linear_constraint(linear, Constraint.Sense.EQ, 3, 'sum1')
penalize = LinearEqualityToPenalty(penalty=1e5)
try:
warnings.filterwarnings(action="ignore",
category=DeprecationWarning)
op2 = penalize.encode(op)
conv = QuadraticProgramToIsing()
qubitop, offset = conv.encode(op2)
finally:
warnings.filterwarnings(action="always",
category=DeprecationWarning)
self.assertEqual(qubitop, QUBIT_OP_MAXIMIZE_SAMPLE)
self.assertEqual(offset, OFFSET_MAXIMIZE_SAMPLE)
def test_valid_variable_type_deprecated(self):
"""Validate the types of the variables for QuadraticProgramToIsing."""
# Integer variable
try:
warnings.filterwarnings(action="ignore",
category=DeprecationWarning)
conv = QuadraticProgramToIsing()
finally:
warnings.filterwarnings(action="always",
category=DeprecationWarning)
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.integer_var(0, 10, "int_var")
_ = conv.encode(op)
# Continuous variable
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.continuous_var(0, 10, "continuous_var")
_ = conv.encode(op)
def test_optimizationproblem_to_ising(self):
""" Test optimization problem to operators"""
op = QuadraticProgram()
for i in range(4):
op.binary_var(name='x{}'.format(i))
linear = {}
for x in op.variables:
linear[x.name] = 1
op.maximize(0, linear, {})
linear = {}
for i, x in enumerate(op.variables):
linear[x.name] = i + 1
op.linear_constraint(linear, Constraint.Sense.EQ, 3, 'sum1')
penalize = LinearEqualityToPenalty()
op2ope = QuadraticProgramToIsing()
op2 = penalize.encode(op)
qubitop, offset = op2ope.encode(op2)
self.assertListEqual(qubitop.paulis, QUBIT_OP_MAXIMIZE_SAMPLE.paulis)
self.assertEqual(offset, OFFSET_MAXIMIZE_SAMPLE)
def test_empty_problem(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_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)
def test_valid_variable_type(self):
"""Validate the types of the variables for QuadraticProgramToIsing."""
# Integer variable
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.integer_var(0, 10, "int_var")
conv = QuadraticProgramToIsing()
_ = conv.encode(op)
# Continuous variable
with self.assertRaises(QiskitOptimizationError):
op = QuadraticProgram()
op.continuous_var(0, 10, "continuous_var")
conv = QuadraticProgramToIsing()
_ = conv.encode(op)
# qc.cx(c[0], c[1])
#Acting the gates on circuit
for i in range (g):
gates()
# Prepare the Ising Hamiltonian
#n = 3 #number of qubits
a = 1.0
k = 2
t = range(1, n+1)
mdl = Model()# build model with docplex
x = [mdl.binary_var() for i in range(n)]
objective = a*(k - mdl.sum(t[i]*x[i] for i in range(n)))**2
mdl.minimize(objective)
qp = QuadraticProgram()# convert to Qiskit's quadratic program
qp.from_docplex(mdl)
qp2ising = QuadraticProgramToIsing()# convert to Ising Hamiltonian
H, offset = qp2ising.encode(qp)
H_matrix = np.real(H.to_matrix())
psi = StateFn(qc)
expectation_value = (~psi @ H @ psi).eval()
print(psi)
print(expectation_value)
# create a QUBO
qubo = QuadraticProgram()
qubo.binary_var('prof')
qubo.binary_var('student')
qubo.binary_var('cat')
qubo.minimize(linear=[10, 5, 0],
quadratic={
('prof', 'student'): 4,
('prof', 'cat'): 2,
('student', 'cat'): -12
})
print(qubo.export_as_lp_string())
# In[7]:
qp2op = QuadraticProgramToIsing()
op, offset = qp2op.encode(qubo)
print('offset: {}'.format(offset))
print('operator:')
print(op.print_details())
# In[8]:
from qiskit.circuit.library import RealAmplitudes
from qiskit.aqua.components.optimizers import COBYLA
qaoa_mes = QAOA(quantum_instance=BasicAer.get_backend('statevector_simulator'))
vqe_mes = VQE(
quantum_instance=BasicAer.get_backend('statevector_simulator'),
var_form=RealAmplitudes(3, reps=2), # parametrized circuit to use