def test_natural_gradient4(self, grad_method, qfi_method, regularization):
"""Test the natural gradient 4"""
# Avoid regularization = lasso intentionally because it does not converge
try:
ham = 0.5 * X - 1 * Z
a = Parameter("a")
params = a
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(a, q[0])
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.0)
nat_grad = NaturalGradient(grad_method=grad_method,
qfi_method=qfi_method,
regularization=regularization).convert(
operator=op, params=params)
values_dict = [{a: np.pi / 4}]
correct_values = [[0.0]] if regularization == "ridge" else [[
-1.41421342
]]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(
nat_grad.assign_parameters(value_dict).eval(),
correct_values[i],
decimal=3)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_natural_gradient(self, method, regularization):
"""Test the natural gradient"""
try:
for params in (ParameterVector("a", 2),
[Parameter("a"), Parameter("b")]):
ham = 0.5 * X - 1 * Z
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(params[0], q[0])
qc.rx(params[1], q[0])
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.0)
nat_grad = NaturalGradient(
grad_method=method,
regularization=regularization).convert(operator=op)
values_dict = [{params[0]: np.pi / 4, params[1]: np.pi / 2}]
# reference values obtained by classically computing the natural gradients
correct_values = [[
-3.26, 1.63
]] if regularization == "ridge" else [[-4.24, 0]]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(
nat_grad.assign_parameters(value_dict).eval(),
correct_values[i],
decimal=1)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_grad_combo_fn_chain_rule_nat_grad(self):
"""Test the chain rule for a custom gradient combo function."""
np.random.seed(2)
def combo_fn(x):
amplitudes = x[0].primitive.data
pdf = np.multiply(amplitudes, np.conj(amplitudes))
return np.sum(np.log(pdf)) / (-len(amplitudes))
def grad_combo_fn(x):
amplitudes = x[0].primitive.data
pdf = np.multiply(amplitudes, np.conj(amplitudes))
grad = []
for prob in pdf:
grad += [-1 / prob]
return grad
try:
qc = RealAmplitudes(2, reps=1)
grad_op = ListOp([StateFn(qc)], combo_fn=combo_fn, grad_combo_fn=grad_combo_fn)
grad = NaturalGradient(grad_method='lin_comb', regularization='ridge'
).convert(grad_op, qc.ordered_parameters)
value_dict = dict(
zip(qc.ordered_parameters, np.random.rand(len(qc.ordered_parameters))))
correct_values = [[0.20777236], [-18.92560338], [-15.89005475], [-10.44002031]]
np.testing.assert_array_almost_equal(grad.assign_parameters(value_dict).eval(),
correct_values, decimal=3)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_natural_gradient(self, method, regularization):
"""Test the natural gradient"""
try:
for params in (ParameterVector('a', 2),
[Parameter('a'), Parameter('b')]):
ham = 0.5 * X - 1 * Z
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(params[0], q[0])
qc.rx(params[1], q[0])
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.)
nat_grad = NaturalGradient(grad_method=method, regularization=regularization)\
.convert(operator=op, params=params)
values_dict = [{params[0]: np.pi / 4, params[1]: np.pi / 2}]
correct_values = [[-2.36003979, 2.06503481]] \
if regularization == 'ridge' else [[-4.2, 0]]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(
nat_grad.assign_parameters(value_dict).eval(),
correct_values[i],
decimal=0)
except MissingOptionalLibraryError as ex:
self.skipTest(str(ex))
def test_natural_gradients_invalid(self):
"""test that an exception is thrown when an invalid gradient method is used"""
solver = VQEUCCFactory(quantum_instance=QuantumInstance(
BasicAer.get_backend("statevector_simulator")))
grad = NaturalGradient(grad_method="fin_diff",
qfi_method="lin_comb_full",
regularization="ridge")
calc = AdaptVQE(self.qubit_converter, solver, gradient=grad)
with self.assertRaises(QiskitNatureError):
_ = calc.solve(self.problem)
def test_gradient_wrapper2(self, backend_type, atol):
"""Test the gradient wrapper for gradients checking that statevector and qasm gives the
same results
dp0/da = cos(a)sin(b) / 2
dp1/da = - cos(a)sin(b) / 2
dp0/db = sin(a)cos(b) / 2
dp1/db = - sin(a)cos(b) / 2
"""
method = "lin_comb"
a = Parameter("a")
b = Parameter("b")
params = [a, b]
qc = QuantumCircuit(2)
qc.h(1)
qc.h(0)
qc.sdg(1)
qc.cz(0, 1)
qc.ry(params[0], 0)
qc.rz(params[1], 0)
qc.h(1)
obs = (Z ^ X) - (Y ^ Y)
op = StateFn(obs, is_measurement=True) @ CircuitStateFn(primitive=qc)
shots = 8192 if backend_type == "qasm_simulator" else 1
values = [[0, np.pi / 2], [np.pi / 4, np.pi / 4],
[np.pi / 3, np.pi / 9]]
correct_values = [[-4.0, 0], [-2.0, -4.82842712],
[-0.68404029, -7.01396121]]
for i, value in enumerate(values):
backend = BasicAer.get_backend(backend_type)
q_instance = QuantumInstance(backend=backend,
shots=shots,
seed_simulator=2,
seed_transpiler=2)
grad = NaturalGradient(grad_method=method).gradient_wrapper(
operator=op, bind_params=params, backend=q_instance)
result = grad(value)
self.assertTrue(np.allclose(result, correct_values[i], atol=atol))
def test_natural_gradient3(self, qfi_method, circuit_qfi):
"""Test the natural gradient 3"""
nat_grad = NaturalGradient(qfi_method=qfi_method)
self.assertIsInstance(nat_grad.qfi_method, circuit_qfi)
def test_natural_gradient2(self):
"""Test the natural gradient 2"""
with self.assertRaises(TypeError):
_ = NaturalGradient().convert(None, None)
def grad_combo_fn(x):
amplitudes = x[0].primitive.data
pdf = np.multiply(amplitudes, np.conj(amplitudes))
grad = []
for prob in pdf:
grad += [-1 / prob]
return grad
qc = RealAmplitudes(2, reps=1)
grad_op = ListOp(
[StateFn(qc.decompose())], combo_fn=combo_fn, grad_combo_fn=grad_combo_fn
)
grad = NaturalGradient(grad_method="lin_comb", regularization="ridge").convert(
grad_op, qc.ordered_parameters
)
value_dict = dict(
zip(qc.ordered_parameters, np.random.rand(len(qc.ordered_parameters)))
)
correct_values = [[0.20777236], [-18.92560338], [-15.89005475], [-10.44002031]]
backend = BasicAer.get_backend("qasm_simulator")
q_instance = QuantumInstance(backend=backend, shots=5000)
sampler = CircuitSampler(backend=q_instance).convert(
grad, params={k: [v] for k, v in value_dict.items()}
)
print('Sampler ', sampler.eval()[0])
print('Correct Values ', correct_values)
