def construct_circuit(self, parameter, statevector_mode=False,
use_simulator_snapshot_mode=False,
circuit_name_prefix=''):
"""
Generate the circuits.
Args:
parameter (numpy.ndarray): parameters for variational form.
statevector_mode (bool, optional): indicate which type of simulator are going to use.
use_simulator_snapshot_mode (bool, optional): is backend from AerProvider,
if True and mode is paulis, single circuit is generated.
circuit_name_prefix (str, optional): a prefix of circuit name
Returns:
list[QuantumCircuit]: the generated circuits with Hamiltonian.
Raises:
AquaError: Circuit cannot be created if an operator has not been provided
"""
if self._initial_state is None:
raise AquaError("Initial state was never provided")
circuits = []
dip_test = self._dip_test(parameter, circuit_name_prefix=circuit_name_prefix)
num_working_qubits = self._num_working_qubits
if not statevector_mode:
creg = ClassicalRegister(dip_test.width(), name="dip_creg")
qreg = find_regs_by_name(dip_test, 'dip_qreg')
dip_test.add_register(creg)
dip_test.barrier(qreg)
dip_test.measure(qreg[:num_working_qubits],
creg[:num_working_qubits])
circuits.append(dip_test)
for i in range(self._num_working_qubits):
# get relevant qubit indices
dip_qubit_idx = [i]
swap_qubit_idx = list(set(range(num_working_qubits)) -
set(dip_qubit_idx))
swap_qubit_idx += [i + num_working_qubits for i in swap_qubit_idx]
dip_qubit_idx += [i + num_working_qubits for i in dip_qubit_idx]
pdip_test = self._pdip_test(parameter,
dip_qubit_idx, swap_qubit_idx,
circuit_name_prefix=circuit_name_prefix)
if not statevector_mode:
creg = ClassicalRegister(pdip_test.width(), name="dip_creg")
qreg = find_regs_by_name(pdip_test, 'pdip_qreg')
pdip_test.add_register(creg)
pdip_test.barrier(qreg)
pdip_test.measure(qreg[swap_qubit_idx + dip_qubit_idx],
creg[swap_qubit_idx + dip_qubit_idx])
circuits.append(pdip_test)
return circuits
def calc_expectataion(self, pauli_str, sub_circuit):
qubit_op = WeightedPauliOperator([[1, Pauli.from_label(pauli_str)]])
sv_mode = False
qi = QuantumInstance(backend=Aer.get_backend('qasm_simulator'),
shots=1000,
seed_simulator=100,
seed_transpiler=2)
if qi.is_statevector:
sv_mode = True
# Make sure that the eval quantum/ classical registers in the circuit are named 'q'/'c'
qc = qubit_op.construct_evaluation_circuit(
statevector_mode=sv_mode,
wave_function=sub_circuit,
qr=find_regs_by_name(sub_circuit, 'q'),
use_simulator_operator_mode=True)
result = qi.execute(qc)
avg, std = qubit_op.evaluate_with_result(
statevector_mode=sv_mode,
result=result,
use_simulator_operator_mode=True)
return avg
def get_optimal_vector(self):
from qiskit.aqua.utils.run_circuits import find_regs_by_name
if 'opt_params' not in self._ret:
raise AquaError("Cannot find optimal vector before running the "
"algorithm to find optimal params.")
qc = self.get_optimal_circuit()
if self._quantum_instance.is_statevector:
ret = self._quantum_instance.execute(qc)
self._ret['min_vector'] = ret.get_statevector(qc)
else:
c = ClassicalRegister(qc.width(), name='c')
q = find_regs_by_name(qc, 'q')
qc.add_register(c)
qc.barrier(q)
qc.measure(q, c)
ret = self._quantum_instance.execute(qc)
self._ret['min_vector'] = ret.get_counts(qc)
return self._ret['min_vector']
def get_optimal_value(self):
"""
Computes the eigenvalue estimates of the initial state
Returns:
numpy.ndarray: array of eigenvalues estimates
"""
if 'opt_params' not in self._ret:
raise AquaError("Cannot find optimal vector before running the "
"algorithm to find optimal params.")
# TODO : Find better way to do this
num_working_qubits = self._num_working_qubits
circuit = self.get_optimal_circuit()
counts = None
if self._quantum_instance.is_statevector:
ret = self._quantum_instance.execute(circuit)
state_vec = Statevector(ret.get_statevector(circuit))
counts = state_vec.probabilities_dict()
else:
c = ClassicalRegister(circuit.width(), name='c')
q = find_regs_by_name(circuit, 'q')
circuit.add_register(c)
circuit.barrier(q)
circuit.measure(q[:num_working_qubits], c[:num_working_qubits])
ret = self._quantum_instance.execute(circuit)
counts = ret.get_counts(circuit)
keys = list(map(''.join, product(['0', '1'],
repeat=num_working_qubits)))
num_shots = sum(counts.values())
probs = dict.fromkeys(keys, 0.0)
for i, (k, v) in enumerate(counts.items()):
probs[k[-num_working_qubits:]] += v / num_shots
print({k: v for k, v in sorted(probs.items(), key=lambda item:
-item[1])})
eigvals = np.array(list(probs.values()))
idx = np.argsort(-eigvals)
return eigvals[idx]
def get_optimal_vector(self) -> Union[List[float], Dict[str, int]]:
"""Get the simulation outcome of the optimal circuit. """
# pylint: disable=import-outside-toplevel
from qiskit.aqua.utils.run_circuits import find_regs_by_name
if 'opt_params' not in self._ret:
raise AquaError("Cannot find optimal vector before running the "
"algorithm to find optimal params.")
qc = self.get_optimal_circuit()
if self._quantum_instance.is_statevector:
ret = self._quantum_instance.execute(qc)
self._ret['min_vector'] = ret.get_statevector(qc)
else:
c = ClassicalRegister(qc.width(), name='c')
q = find_regs_by_name(qc, 'q')
qc.add_register(c)
qc.barrier(q)
qc.measure(q, c)
ret = self._quantum_instance.execute(qc)
self._ret['min_vector'] = ret.get_counts(qc)
return self._ret['min_vector']
def construct_evaluation_circuit(self, wave_function, statevector_mode,
qr=None, cr=None, use_simulator_snapshot_mode=False,
circuit_name_prefix=''):
r"""
Construct the circuits for evaluation, which calculating the expectation <psi\|H\|psi>.
At statevector mode: to simplify the computation, we do not build the whole
circuit for <psi|H|psi>, instead of
that we construct an individual circuit <psi\|, and a bundle circuit for H\|psi>
Args:
wave_function (QuantumCircuit): the quantum circuit.
statevector_mode (bool): indicate which type of simulator are going to use.
qr (QuantumRegister, optional): the quantum register associated with the input_circuit
cr (ClassicalRegister, optional): the classical register associated
with the input_circuit
use_simulator_snapshot_mode (bool, optional): if aer_provider is used, we can do faster
evaluation for pauli mode on
statevector simulation
circuit_name_prefix (str, optional): a prefix of circuit name
Returns:
list[QuantumCircuit]: a list of quantum circuits and each circuit with a unique name:
circuit_name_prefix + Pauli string
Raises:
AquaError: if Operator is empty
AquaError: if quantum register is not provided explicitly and
cannot find quantum register with `q` as the name
AquaError: The provided qr is not in the wave_function
"""
if self.is_empty():
raise AquaError("Operator is empty, check the operator.")
# pylint: disable=import-outside-toplevel
from qiskit.aqua.utils.run_circuits import find_regs_by_name
if qr is None:
qr = find_regs_by_name(wave_function, 'q')
if qr is None:
raise AquaError("Either provide the quantum register (qr) explicitly or use"
" `q` as the name of the quantum register in the input circuit.")
else:
if not wave_function.has_register(qr):
raise AquaError("The provided QuantumRegister (qr) is not in the circuit.")
n_qubits = self.num_qubits
instructions = self.evaluation_instruction(statevector_mode, use_simulator_snapshot_mode)
circuits = []
if use_simulator_snapshot_mode:
circuit = wave_function.copy(name=circuit_name_prefix + 'snapshot_mode')
# Add expectation value snapshot instruction
instr = instructions.get('expval_snapshot', None)
if instr is not None:
circuit.append(instr, qr)
circuits.append(circuit)
elif statevector_mode:
circuits.append(wave_function.copy(name=circuit_name_prefix + 'psi'))
for _, pauli in self._paulis:
inst = instructions.get(pauli.to_label(), None)
if inst is not None:
circuit = wave_function.copy(name=circuit_name_prefix + pauli.to_label())
circuit.append(inst, qr)
circuits.append(circuit)
else:
base_circuit = wave_function.copy()
if cr is not None:
if not base_circuit.has_register(cr):
base_circuit.add_register(cr)
else:
cr = find_regs_by_name(base_circuit, 'c', qreg=False)
if cr is None:
cr = ClassicalRegister(n_qubits, name='c')
base_circuit.add_register(cr)
for basis, _ in self._basis:
circuit = base_circuit.copy(name=circuit_name_prefix + basis.to_label())
circuit.append(instructions[basis.to_label()], qargs=qr, cargs=cr)
circuits.append(circuit)
return circuits
