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_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 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])