def get_state(self,circuit:Circuit, fit_to_constraints=True) -> list:
c = circuit.copy()
if fit_to_constraints :
Transform.RebaseToProjectQ().apply(c)
fwd = ForwarderEngine(self._backend)
eng = MainEngine(backend=self._backend,engine_list=[fwd])
qureg = eng.allocate_qureg(c.n_qubits)
tk_to_projectq(eng,qureg,c)
eng.flush()
state = self._backend.cheat()[1] #`cheat()` returns tuple:(a dictionary of qubit indices, statevector)
All(Measure) | qureg
return state #list of complex numbers
def projectq_expectation_value(circuit:Circuit,hamiltonian:QubitOperator) -> float :
ProjectQback = Simulator()
fwd = ForwarderEngine(ProjectQback)
eng = MainEngine(backend=ProjectQback,engine_list=[fwd])
qureg = eng.allocate_qureg(circuit.n_qubits)
c = circuit.copy()
Transform.RebaseToProjectQ().apply(c)
tk_to_projectq(eng,qureg,c)
eng.flush()
energy = eng.backend.get_expectation_value(hamiltonian,qureg)
All(Measure) | qureg
return energy
def _optimise(self): #takes the circuit and optimises it before regurgitating it as a series of ProjectQ commands
if self._circuit.n_qubits!=0:
Transform.OptimisePhaseGadgets().apply(self._circuit)
Transform.RebaseToProjectQ().apply(self._circuit)
cmd_list = []
for i in range(self._circuit.n_qubits):
gate = pqo.Allocate
cmd = ProjectQCommand(self.main_engine,gate,gate.make_tuple_of_qureg(self._qubit_dictionary[i]))
cmd_list.append(cmd)
if self._circuit.n_gates==0:
return cmd_list
for command in self._circuit:
cmd = _get_pq_command_from_tk_command(command,self.main_engine,self._qubit_dictionary)
cmd_list.append(cmd)
return cmd_list