def state_tomography(qc, qubit_list):
"""
A function for the state tomography for the given circuit
which the qubit_list are the qubits for the main system
Parameters
qc[QuantumCircuit]: a quantum circuit
qubit_list[list]: a list of qubits in the system
"""
qc_tomo = state_tomography_circuits(qc, qubit_list)
job = execute(qc_tomo, Aer.get_backend('qasm_simulator'), shots=10000)
result = job.result()
state_tomo = StateTomographyFitter(result, qc_tomo)
state_tomo_fit = state_tomo.fit()
return state_tomo_fit
def _state_tomography(self):
"""The state tomography.
The HHL result gets extracted via state tomography. Available for
qasm simulator and real hardware backends.
"""
# Preparing the state tomography circuits
tomo_circuits = state_tomography_circuits(self._circuit,
self._io_register)
tomo_circuits_noanc = deepcopy(tomo_circuits)
ca = ClassicalRegister(1)
for circ in tomo_circuits:
circ.add_register(ca)
circ.measure(self._reciprocal._anc, ca[0])
# Extracting the probability of successful run
results = self._quantum_instance.execute(tomo_circuits)
probs = []
for circ in tomo_circuits:
counts = results.get_counts(circ)
s, f = 0, 0
for k, v in counts.items():
if k[0] == "1":
s += v
else:
f += v
probs.append(s/(f+s))
probs = self._resize_vector(probs)
self._ret["probability_result"] = np.real(probs)
# Filtering the tomo data for valid results with ancillary measured
# to 1, i.e. c1==1
results_noanc = self._tomo_postselect(results)
tomo_data = StateTomographyFitter(results_noanc, tomo_circuits_noanc)
rho_fit = tomo_data.fit()
vec = np.sqrt(np.diag(rho_fit))
self._hhl_results(vec)
def createQC2(r):
#parameter setting lis
paraLis16 = [0.196, 0.379, 0.981, 0.589, 1.178, 16]
paraLis8 = [1.963, 1.115, 1.963, 2.615, 1.178, 8]
paraLis4 = [-0.785, 1.017, 3.927, 2.517, 2.356, 4]
paraLis2 = [-9.014 * 10**(-9), -0.75, 1.571, 0.75, -1.571, 2]
paraLis = [paraLis16, paraLis8, paraLis4, paraLis2]
#Register and circuit
s = QuantumRegister(1, name='s')
j = QuantumRegister(4, name='j')
q = QuantumRegister(2, name='q')
#cr = ClassicalRegister(1,name='cr')
#crtmp= ClassicalRegister(2,name='crtmp')
qc = QuantumCircuit(s, j, q)
#Gate preparation
qft = QFT(4, inverse=False)
iqft = QFT(4, inverse=True)
gateLis = []
gateinvLis = []
for i in range(4):
toPut = Agate(paraLis[i]).control()
gateLis.append(toPut)
qc.h(j)
qc.h(q)
for i in range(4):
gate = gateLis[i]
qc.append(gate, qargs=[j[i]] + q[:])
qc.append(iqft, qargs=j[:])
#qc.swap(j[1],j[3])
for i in range(4):
angle = 2**(3 - i) * np.pi
angle *= 2**(-r + 1)
qc.cry(angle, j[i], s[0])
qc.append(qft, qargs=j[:])
for i in range(4):
gate = gateLis[3 - i].inverse()
qc.append(gate, qargs=[j[3 - i]] + q[:])
qc.barrier()
qc.h(j)
#qc.measure(s,cr)
qc.barrier()
print(qc)
tomo_circuits = state_tomography_circuits(qc, q)
tomo_circuits_noanc = deepcopy(tomo_circuits)
to_put = ClassicalRegister(1)
for i in tomo_circuits:
i.add_register(to_put)
i.measure(s, to_put[0])
print(i)
backend = Aer.get_backend('qasm_simulator')
results = execute(tomo_circuits, backend, shots=10**5).result()
print(results.get_counts())
probs = []
for circ in tomo_circuits:
counts = results.get_counts(circ)
s, f = 0, 0
for k, v in counts.items():
if k[0] == "1":
s += v
else:
f += v
probs.append(s / (f + s))
#results_noanc = tomo_postselect(results)
data = StateTomographyFitter(results, tomo_circuits)
print(data)
#omo_data = StateTomographyFitter(results_noanc,tomo_circuits_noanc)
rho_fit = data.fit()
print(rho_fit)
'''
# exec state tomography experiments
job = execute(tomo_circuits, backend = backend_qasm, shots = 8192)
# In[10]:
job.status()
# In[51]:
# get rho(density matrix) from tomography data
tomo_data = StateTomographyFitter(job.result(), tomo_circuits)
rho = tomo_data.fit()
psi = get_psi(rho)
new_params = optimize.fmin(diff(psi), params) # minimize 1 - |<Psi|RY(theta1,..,theta4)>|^2
print("new params",new_params)
new_params
# In[59]:
new_Hamiltonian = eval_H(psi, h_matrix)
print("new_Hamiltonian",new_Hamiltonian)
# In[58]: