def _routed_ibmq_circuit(circuit: Circuit,
arc: Architecture) -> QuantumCircuit:
c = circuit.copy()
Transform.RebaseToQiskit().apply(c)
physical_c = route(c, arc)
physical_c.decompose_SWAP_to_CX()
physical_c.redirect_CX_gates(arc)
Transform.OptimisePostRouting().apply(physical_c)
qc = tk_to_qiskit(physical_c)
return qc
def run(self,
circuit: Circuit,
shots: int,
fit_to_constraints=True,
seed: int = None) -> np.ndarray:
"""Run a circuit on Qiskit Aer Qasm simulator.
:param circuit: The circuit to run
:type circuit: Circuit
:param shots: Number of shots (repeats) to run
:type shots: int
:param fit_to_constraints: Compile the circuit to meet the constraints of the backend, defaults to True
:type fit_to_constraints: bool, optional
:param seed: random seed to for simulator
:type seed: int
:return: Table of shot results, each row is a shot, columns are ordered by qubit ordering. Values are 0 or 1, corresponding to qubit basis states.
:rtype: numpy.ndarray
"""
c = circuit.copy()
if fit_to_constraints:
Transform.RebaseToQiskit().apply(c)
dag = tk_to_dagcircuit(c)
qc = dag_to_circuit(dag)
qobj = assemble(qc, shots=shots, seed_simulator=seed, memory=True)
job = self._backend.run(qobj, noise_model=self.noise_model)
shot_list = job.result().get_memory(qc)
return np.asarray([_convert_bin_str(shot) for shot in shot_list])
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
def routed_ibmq_circuit(circuit: Circuit, arc: Architecture) -> QuantumCircuit:
physical_c = route(circuit, arc)
physical_c.decompose_SWAP_to_CX()
physical_c.redirect_CX_gates(arc)
Transform.OptimisePostRouting().apply(physical_c)
dag = tk_to_dagcircuit(physical_c)
qc = dag_to_circuit(dag)
return qc
def get_state(self, circuit, fit_to_constraints=True):
"""Calculate the statevector for a circuit.
:param circuit: circuit to calculate
:return: complex numpy array of statevector
"""
c = circuit.copy()
if fit_to_constraints:
Transform.RebaseToQuil().apply(c)
p = tk_to_pyquil(c)
wf = self._sim.wavefunction(p)
return wf.amplitudes
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 get_operator_expectation_value(self,
state_circuit,
operator,
shots=1000):
c = state_circuit.copy()
Transform.RebaseToQuil().apply(c)
prog = tk_to_pyquil(c)
pauli_sum = PauliSum([
_gen_PauliTerm(term, coeff)
for term, coeff in operator.terms.items()
])
return self._sim.expectation(prog, pauli_sum)
def run(self,
circuit: Circuit,
shots: int,
fit_to_constraints: bool = True) -> np.ndarray:
"""Run a circuit on the Rigetti QVM or a QCS device.
:param circuit: The circuit to run
:param shots: Number of shots (repeats) to run
:param fit_to_constraints: Compile the circuit to meet the constraints of the backend, defaults to True
:return: Table of shot results, each row is a shot, columns are ordered by qubit ordering. Values are 0 or 1, corresponding to qubit basis states.
"""
c = circuit.copy()
if fit_to_constraints:
phys_c = route(c, self._architecture)
phys_c.decompose_SWAP_to_CX()
Transform.OptimisePostRouting().apply(phys_c)
Transform.RebaseToQuil().apply(c)
p = tk_to_pyquil(c)
p.wrap_in_numshots_loop(shots)
ex = self._qc.compiler.native_quil_to_executable(p)
return np.asarray(self._qc.run(ex))
def process_circ(self, circ):
num_qubits = circ.n_qubits
if num_qubits == 1 or self.coupling_map == "all-to-all":
coupling_map = None
else:
coupling_map = self.coupling_map
# pre-routing optimise
Transform.OptimisePhaseGadgets().apply(circ)
circlay = list(range(num_qubits))
if coupling_map:
directed_arc = Architecture(coupling_map)
# route_ibm fnction that takes directed Arc, returns dag with cnots etc.
circ, circlay = route(circ,directed_arc)
circ.apply_boundary_map(circlay[0])
# post route optimise
Transform.OptimisePostRouting().apply(circ)
circ.remove_blank_wires()
return circ, circlay
def get_state(self, circuit, fit_to_constraints=True) :
"""
Calculate the statevector for a circuit.
:param circuit: circuit to calculate
:return: complex numpy array of statevector
"""
c = circuit.copy()
if fit_to_constraints :
Transform.RebaseToQiskit().apply(c)
qc = tk_to_qiskit(c)
qobj = assemble(qc)
job = self._backend.run(qobj)
return np.asarray(job.result().get_statevector(qc, decimals=16))
def get_unitary(self, circuit, fit_to_constraints=True) :
"""
Obtains the unitary for a given quantum circuit
:param circuit: The circuit to inspect
:type circuit: Circuit
:param fit_to_constraints: Forces the circuit to be decomposed into the correct gate set, defaults to True
:type fit_to_constraints: bool, optional
:return: The unitary of the circuit. Qubits are ordered with qubit 0 as the least significant qubit
"""
c = circuit.copy()
if fit_to_constraints :
Transform.RebaseToQiskit().apply(c)
qc = tk_to_qiskit(c)
qobj = assemble(qc)
job = self._backend.run(qobj)
return job.result().get_unitary(qc)
def get_counts(self, circuit, shots, fit_to_constraints=True, seed=None) :
"""
Run the circuit on the backend and accumulate the results into a summary of counts
:param circuit: The circuit to run
:param shots: Number of shots (repeats) to run
:param fit_to_constraints: Compile the circuit to meet the constraints of the backend, defaults to True
:param seed: Random seed to for simulator
:return: Dictionary mapping bitvectors of results to number of times that result was observed (zero counts are omitted)
"""
c = circuit.copy()
if fit_to_constraints :
Transform.RebaseToQiskit().apply(c)
qc = tk_to_qiskit(c)
qobj = assemble(qc, shots=shots, seed_simulator=seed)
job = self._backend.run(qobj, noise_model=self.noise_model)
counts = job.result().get_counts(qc)
return {tuple(_convert_bin_str(b)) : c for b, c in counts.items()}
print (qubit_generator)
for pauli, coeff in qubit_generator.terms.items():
pauli_evolution(pauli, coeff, circ)
print("Pre-Opt Qasm Circuit")
dag = tk_to_dagcircuit(circ)
print(dag.qasm(qeflag=True))
print(circ.depth())
qc = dag_to_circuit(dag)
print(qc)
#optimise_pre_routing(circ)
Transform.get_transform_instance("PauliGadget_Opt").apply(circ)
optimise_post_routing(circ)
print("Post-Opt Qasm Circuit")
dag = tk_to_dagcircuit(circ)
print(dag.qasm(qeflag=True))
print(circ.depth())
qc = dag_to_circuit(dag)
print(qc)
energy = 0
for pauli, coeff in qubit_hamiltonian.terms.items():
shot_exp = backend.get_pauli_expectation_value(circ, pauli, 8000)
shot_energy = coeff*shot_exp
def get_pauli_expectation_value(self, state_circuit, pauli, shots=1000):
c = state_circuit.copy()
Transform.RebaseToQuil().apply(c)
prog = tk_to_pyquil(c)
pauli_term = _gen_PauliTerm(pauli)
return self._sim.expectation(prog, [pauli_term])
