def test_gradient_rzz(self, method):
# pylint: disable=wrong-spelling-in-comment
"""Test the state gradient for ZZ rotation
"""
ham = Z ^ X
a = Parameter('a')
q = QuantumRegister(2)
qc = QuantumCircuit(q)
qc.h(q[0])
qc.rzz(a, q[0], q[1])
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.)
params = [a]
state_grad = Gradient(grad_method=method).convert(operator=op,
params=params)
values_dict = [{a: np.pi / 4}, {a: np.pi / 2}]
correct_values = [[-0.707], [-1.]]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(
state_grad.assign_parameters(value_dict).eval(),
correct_values[i],
decimal=1)
def test_operator_coefficient_gradient(self, method):
"""Test the operator coefficient gradient
Tr( | psi > < psi | Z) = sin(a)sin(b)
Tr( | psi > < psi | X) = cos(a)
"""
a = Parameter('a')
b = Parameter('b')
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.rz(a, q[0])
qc.rx(b, q[0])
coeff_0 = Parameter('c_0')
coeff_1 = Parameter('c_1')
ham = coeff_0 * X + coeff_1 * Z
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.0)
gradient_coeffs = [coeff_0, coeff_1]
coeff_grad = Gradient(grad_method=method).convert(op, gradient_coeffs)
values_dict = [{
coeff_0: 0.5,
coeff_1: -1,
a: np.pi / 4,
b: np.pi
}, {
coeff_0: 0.5,
coeff_1: -1,
a: np.pi / 4,
b: np.pi / 4
}]
correct_values = [[1 / np.sqrt(2), 0], [1 / np.sqrt(2), 1 / 2]]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(
coeff_grad.assign_parameters(value_dict).eval(),
correct_values[i],
decimal=1)
def evaluate_operators(
self, state: Union[str, dict, Result, list, np.ndarray, Statevector,
QuantumCircuit, Instruction, OperatorBase],
operators: Union[WeightedPauliOperator, OperatorBase, list, dict]
) -> Union[float, List[float], Dict[str, List[float]]]:
"""Evaluates additional operators at the given state.
Args:
state: any kind of input that can be used to specify a state. See also ``StateFn`` for
more details.
operators: either a single, list or dictionary of ``WeightedPauliOperator``s or any kind
of operator implementing the ``OperatorBase``.
Returns:
The expectation value of the given operator(s). The return type will be identical to the
format of the provided operators.
"""
# try to get a QuantumInstance from the solver
quantum_instance = getattr(self._solver, 'quantum_instance', None)
if not isinstance(state, StateFn):
state = StateFn(state)
# handle all possible formats of operators
# i.e. if a user gives us a dict of operators, we return the results equivalently, etc.
if isinstance(operators, list):
results = []
for op in operators:
results.append(self._eval_op(state, op, quantum_instance))
elif isinstance(operators, dict):
results = {} # type: ignore
for name, op in operators.items():
results[name] = self._eval_op(state, op, quantum_instance)
else:
results = self._eval_op(state, operators, quantum_instance)
return results
def construct_circuit(
self, parameter: Union[List[float], List[Parameter], np.ndarray]
) -> OperatorBase:
r"""
Generate the ansatz circuit and expectation value measurement, and return their
runnable composition.
Args:
parameter: Parameters for the ansatz circuit.
Returns:
The Operator equalling the measurement of the ansatz :class:`StateFn` by the
Observable's expectation :class:`StateFn`.
Raises:
AquaError: If no operator has been provided.
"""
if self.operator is None:
raise AquaError("The operator was never provided.")
# ensure operator and varform are compatible
self._check_operator_varform()
if isinstance(self.var_form, QuantumCircuit):
param_dict = dict(zip(self._var_form_params, parameter))
wave_function = self.var_form.assign_parameters(param_dict)
else:
wave_function = self.var_form.construct_circuit(parameter)
# If ExpectationValue was never created, create one now.
if not self.expectation:
self._try_set_expectation_value_from_factory()
observable_meas = self.expectation.convert(
StateFn(self.operator, is_measurement=True))
ansatz_circuit_op = CircuitStateFn(wave_function)
return observable_meas.compose(ansatz_circuit_op).reduce()
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
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)
def test_gradient_p(self, method):
"""Test the state gradient for p
|psi> = 1/sqrt(2)[[1, exp(ia)]]
Tr(|psi><psi|Z) = 0
Tr(|psi><psi|X) = cos(a)
d<H>/da = - 0.5 sin(a)
"""
ham = 0.5 * X - 1 * Z
a = Parameter('a')
params = a
q = QuantumRegister(1)
qc = QuantumCircuit(q)
qc.h(q)
qc.p(a, q[0])
op = ~StateFn(ham) @ CircuitStateFn(primitive=qc, coeff=1.)
state_grad = Gradient(grad_method=method).convert(operator=op, params=params)
values_dict = [{a: np.pi / 4}, {a: 0}, {a: np.pi / 2}]
correct_values = [-0.5 / np.sqrt(2), 0, -0.5]
for i, value_dict in enumerate(values_dict):
np.testing.assert_array_almost_equal(state_grad.assign_parameters(value_dict).eval(),
correct_values[i], decimal=1)
def test_pauli_expect_op_vector(self):
""" pauli expect op vector test """
paulis_op = ListOp([X, Y, Z, I])
converted_meas = self.expect.convert(~StateFn(paulis_op))
plus_mean = (converted_meas @ Plus)
sampled_plus = self.sampler.convert(plus_mean)
np.testing.assert_array_almost_equal(sampled_plus.eval(), [1, 0, 0, 1], decimal=1)
minus_mean = (converted_meas @ Minus)
sampled_minus = self.sampler.convert(minus_mean)
np.testing.assert_array_almost_equal(sampled_minus.eval(), [-1, 0, 0, 1], decimal=1)
zero_mean = (converted_meas @ Zero)
sampled_zero = self.sampler.convert(zero_mean)
np.testing.assert_array_almost_equal(sampled_zero.eval(), [0, 0, 1, 1], decimal=1)
sum_zero = (Plus + Minus) * (.5 ** .5)
sum_zero_mean = (converted_meas @ sum_zero)
sampled_zero_mean = self.sampler.convert(sum_zero_mean)
# !!NOTE!!: Depolarizing channel (Sampling) means interference
# does not happen between circuits in sum, so expectation does
# not equal expectation for Zero!!
np.testing.assert_array_almost_equal(sampled_zero_mean.eval(), [0, 0, 0, 2], decimal=1)
# 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)
def _parameter_shift(
self, operator: OperatorBase, params: Union[ParameterExpression,
ParameterVector,
List]) -> OperatorBase:
r"""
Args:
operator: The operator containing circuits we are taking the derivative of.
params: The parameters (ω) we are taking the derivative with respect to. If
a ParameterVector is provided, each parameter will be shifted.
Returns:
param_shifted_op: An operator object which evaluates to the respective gradients.
Raises:
ValueError: If the given parameters do not occur in the provided operator
TypeError: If the operator has more than one circuit representing the quantum state
"""
if isinstance(params, (ParameterVector, list)):
param_grads = [
self._parameter_shift(operator, param) for param in params
]
absent_params = [
params[i] for i, grad_ops in enumerate(param_grads)
if grad_ops is None
]
if len(absent_params) > 0:
raise ValueError(
"The following parameters do not appear in the provided operator: ",
absent_params)
return ListOp(absent_params)
# By this point, it's only one parameter
param = params
if not isinstance(param, ParameterExpression):
raise ValueError
if isinstance(operator,
ListOp) and not isinstance(operator, ComposedOp):
return_op = operator.traverse(
partial(self._parameter_shift, params=param))
# Remove any branch of the tree where the relevant parameter does not occur
trimmed_oplist = [op for op in return_op.oplist if op is not None]
# If all branches are None, remove the parent too
if len(trimmed_oplist) == 0:
return None
# Rebuild the operator with the trimmed down oplist
properties = {
'coeff': return_op._coeff,
'abelian': return_op._abelian
}
if return_op.__class__ == ListOp:
properties['combo_fn'] = return_op.combo_fn
return return_op.__class__(oplist=trimmed_oplist, **properties)
else:
circs = self.get_unique_circuits(operator)
if len(circs) > 1:
raise TypeError(
'Please define an operator with a single circuit representing '
'the quantum state.')
if len(circs) == 0:
return operator
circ = circs[0]
if self.analytic:
# Unroll the circuit into a gate set for which the gradient may be computed
# using pi/2 shifts.
circ = ParamShift._unroll_to_supported_operations(circ)
operator = ParamShift._replace_operator_circuit(operator, circ)
if param not in circ._parameter_table:
return ~Zero @ One
shifted_ops = []
summed_shifted_op = None
for m, param_occurence in enumerate(circ._parameter_table[param]):
param_index = param_occurence[1]
pshift_op = deepcopy(operator)
mshift_op = deepcopy(operator)
# We need the circuit objects of the newly instantiated operators
pshift_circ = self.get_unique_circuits(pshift_op)[0]
mshift_circ = self.get_unique_circuits(mshift_op)[0]
pshift_gate = pshift_circ._parameter_table[param][m][0]
mshift_gate = mshift_circ._parameter_table[param][m][0]
p_param = pshift_gate.params[param_index]
m_param = mshift_gate.params[param_index]
# For analytic gradients the circuit parameters are shifted once by +pi/2 and
# once by -pi/2.
if self.analytic:
shift_constant = 0.5
pshift_gate.params[param_index] = (p_param +
(np.pi /
(4 * shift_constant)))
mshift_gate.params[param_index] = (m_param -
(np.pi /
(4 * shift_constant)))
# For finite difference gradients the circuit parameters are shifted once by
# +epsilon and once by -epsilon.
else:
shift_constant = 1. / (2 * self._epsilon)
pshift_gate.params[param_index] = (p_param + self._epsilon)
mshift_gate.params[param_index] = (m_param - self._epsilon)
# The results of the shifted operators are now evaluated according the parameter
# shift / finite difference formula.
if isinstance(operator, ComposedOp):
shifted_op = shift_constant * (pshift_op - mshift_op)
# If the operator represents a quantum state then we apply a special combo
# function to evaluate probability gradients.
elif isinstance(operator, StateFn):
shifted_op = ListOp([pshift_op, mshift_op],
combo_fn=partial(
self._prob_combo_fn,
shift_constant=shift_constant))
else:
raise TypeError(
'Probability gradients are not supported for the given '
'operator type')
if isinstance(p_param, ParameterExpression) and not isinstance(
p_param, Parameter):
expr_grad = DerivativeBase.parameter_expression_grad(
p_param, param)
shifted_op *= expr_grad
if not summed_shifted_op:
summed_shifted_op = shifted_op
else:
summed_shifted_op += shifted_op
shifted_ops.append(summed_shifted_op)
if not SummedOp(shifted_ops).reduce():
return ~StateFn(Zero) @ One
else:
return SummedOp(shifted_ops).reduce()
def _run(self) -> 'VQEResult':
"""Run the algorithm to compute the minimum eigenvalue.
Returns:
The result of the VQE algorithm as ``VQEResult``.
Raises:
AquaError: Wrong setting of operator and backend.
"""
if self.operator is None:
raise AquaError("The operator was never provided.")
self._check_operator_varform()
self._quantum_instance.circuit_summary = True
self._eval_count = 0
# Convert the gradient operator into a callable function that is compatible with the
# optimization routine.
if self._gradient:
if isinstance(self._gradient, GradientBase):
self._gradient = self._gradient.gradient_wrapper(
~StateFn(self._operator) @ StateFn(self._var_form),
bind_params=self._var_form_params,
backend=self._quantum_instance)
vqresult = self.find_minimum(initial_point=self.initial_point,
var_form=self.var_form,
cost_fn=self._energy_evaluation,
gradient_fn=self._gradient,
optimizer=self.optimizer)
# TODO remove all former dictionary logic
self._ret = {}
self._ret['num_optimizer_evals'] = vqresult.optimizer_evals
self._ret['min_val'] = vqresult.optimal_value
self._ret['opt_params'] = vqresult.optimal_point
self._ret['eval_time'] = vqresult.optimizer_time
self._ret['opt_params_dict'] = vqresult.optimal_parameters
if self._ret['num_optimizer_evals'] is not None and \
self._eval_count >= self._ret['num_optimizer_evals']:
self._eval_count = self._ret['num_optimizer_evals']
self._eval_time = self._ret['eval_time']
logger.info('Optimization complete in %s seconds.\nFound opt_params %s in %s evals',
self._eval_time, self._ret['opt_params'], self._eval_count)
self._ret['eval_count'] = self._eval_count
result = VQEResult()
result.combine(vqresult)
result.eigenvalue = vqresult.optimal_value + 0j
result.eigenstate = self.get_optimal_vector()
self._ret['energy'] = self.get_optimal_cost()
self._ret['eigvals'] = np.asarray([self._ret['energy']])
self._ret['eigvecs'] = np.asarray([result.eigenstate])
if len(self.aux_operators) > 0:
self._eval_aux_ops()
# TODO remove when ._ret is deprecated
result.aux_operator_eigenvalues = self._ret['aux_ops'][0]
result.cost_function_evals = self._eval_count
return result
def _run(self):
"""
Run the algorithm to compute up to the requested k number of eigenvalues.
Returns:
dict: Dictionary of results
Raises:
AquaError: if no operator has been provided
"""
if self._operator is None:
raise AquaError("Operator was never provided")
k_orig = self._k
if self._filter_criterion:
# need to consider all elements if a filter is set
self._k = 2**(self._operator.num_qubits)
self._ret = {}
self._solve()
# compute energies before filtering, as this also evaluates the aux operators
self._get_energies()
# if a filter is set, loop over the given values and only keep
if self._filter_criterion:
eigvecs = []
eigvals = []
energies = []
aux_ops = []
cnt = 0
for i in range(len(self._ret['eigvals'])):
eigvec = self._ret['eigvecs'][i]
eigval = self._ret['eigvals'][i]
energy = self._ret['energies'][i]
if 'aux_ops' in self._ret:
aux_op = self._ret['aux_ops'][i]
else:
aux_op = None
if self._filter_criterion(eigvec, eigval, aux_op):
cnt += 1
eigvecs += [eigvec]
eigvals += [eigval]
energies += [energy]
if 'aux_ops' in self._ret:
aux_ops += [aux_op]
if cnt == k_orig:
break
self._ret['eigvecs'] = np.array(eigvecs)
self._ret['eigvals'] = np.array(eigvals)
self._ret['energies'] = np.array(energies)
self._k = k_orig
# evaluate ground state after filtering (in case a filter is set)
self._get_ground_state_energy()
logger.debug('NumPyEigensolver _run result:\n%s',
pprint.pformat(self._ret, indent=4))
result = EigensolverResult()
if 'eigvals' in self._ret:
result.eigenvalues = self._ret['eigvals']
if 'eigvecs' in self._ret:
result.eigenstates = ListOp([StateFn(vec) for vec in self._ret['eigvecs']])
if 'aux_ops' in self._ret:
result.aux_operator_eigenvalues = self._ret['aux_ops']
logger.debug('EigensolverResult dict:\n%s',
pprint.pformat(result.data, indent=4))
return result
def test_parameterized_qobj(self):
""" grouped pauli expectation test """
two_qubit_h2 = (-1.052373245772859 * I ^ I) + \
(0.39793742484318045 * I ^ Z) + \
(-0.39793742484318045 * Z ^ I) + \
(-0.01128010425623538 * Z ^ Z) + \
(0.18093119978423156 * X ^ X)
aer_sampler = CircuitSampler(self.sampler.quantum_instance,
param_qobj=True,
attach_results=True)
var_form = RealAmplitudes()
var_form.num_qubits = 2
observable_meas = self.expect.convert(
StateFn(two_qubit_h2, is_measurement=True))
ansatz_circuit_op = CircuitStateFn(var_form)
expect_op = observable_meas.compose(ansatz_circuit_op).reduce()
def generate_parameters(num):
param_bindings = {}
for param in var_form.parameters:
values = []
for _ in range(num):
values.append(np.random.rand())
param_bindings[param] = values
return param_bindings
def validate_sampler(ideal, sut, param_bindings):
expect_sampled = ideal.convert(expect_op,
params=param_bindings).eval()
actual_sampled = sut.convert(expect_op,
params=param_bindings).eval()
self.assertAlmostEqual(actual_sampled, expect_sampled, delta=.1)
def get_circuit_templates(sampler):
return sampler._transpiled_circ_templates
def validate_aer_binding_used(templates):
self.assertIsNotNone(templates)
def validate_aer_templates_reused(prev_templates, cur_templates):
self.assertIs(prev_templates, cur_templates)
validate_sampler(self.sampler, aer_sampler, generate_parameters(1))
cur_templates = get_circuit_templates(aer_sampler)
validate_aer_binding_used(cur_templates)
prev_templates = cur_templates
validate_sampler(self.sampler, aer_sampler, generate_parameters(2))
cur_templates = get_circuit_templates(aer_sampler)
validate_aer_templates_reused(prev_templates, cur_templates)
prev_templates = cur_templates
validate_sampler(self.sampler, aer_sampler,
generate_parameters(2)) # same num of params
cur_templates = get_circuit_templates(aer_sampler)
validate_aer_templates_reused(prev_templates, cur_templates)
qc.y(0)
operations_i.append('qc_new.y(0)')
elif param == 5:
qc.z(0)
operations_i.append('qc_new.z(0)')
print(A)
qc.x(2)
qc.z(1)
qc.y(3)
qc.s(0)
qc.cx(0, 1)
# qc.swap(1, 3)
qc.cx(2, 3)
cliff_sf_target = StateFn(qc)
print(qc)
# %% randomly select a single qubit gate and randomly change it to another
# calculate overlap integral
def overlap_modules_square(psi_2):
psi_1 = cliff_sf_target
# return (psi_1.adjoint().compose(psi_2).eval().real) * (psi_2.adjoint().compose(psi_1).eval().real)
return np.real((~psi_1 @ psi_2).eval() * (~psi_2 @ psi_1).eval())
# radom apply GATE_FUNCTIONS
applied_gates = []
qp = QuadraticProgram() # convert to Qiskit's quadratic program
qp.from_docplex(mdl)
qp2ising = QuadraticProgramToIsing() # convert to Ising Hamiltonian
H, offset = qp2ising.encode(qp)
op = H.to_pauli_op()
help(H)
print(H)
# Prepare the state
qc = QuantumCircuit(3)
qc.h(0)
qc.cx(0, 1)
qc.cy(1, 2)
psi = StateFn(qc) # wrap it into a statefunction
print(psi)
# Calculate the expectation value for Ising Hamiltonian
print('expectation_value:', psi.adjoint().compose(op).compose(psi).eval().real)
# %% another way of calculating expectation
# define your backend or quantum instance (where you can add settings)
backend = Aer.get_backend('qasm_simulator')
q_instance = QuantumInstance(backend, shots=1024)
# define the state to sample
measurable_expression = StateFn(op, is_measurement=True).compose(psi)
