def test_purity(self):
rho1 = [[1, 0], [0, 0]]
rho2 = [[0.5, 0], [0, 0.5]]
rho3 = 0.7 * np.array(rho1) + 0.3 * np.array(rho2)
test_pass = (purity(rho1) == 1.0 and purity(rho2) == 0.5
and round(purity(rho3), 10) == 0.745)
self.assertTrue(test_pass)
def test_purity(self):
rho1 = [[1, 0], [0, 0]]
rho2 = [[0.5, 0], [0, 0.5]]
rho3 = 0.7 * np.array(rho1) + 0.3 * np.array(rho2)
test_pass = purity(rho1) == 1.0 and \
purity(rho2) == 0.5 and \
round(purity(rho3), 10) == 0.745
self.assertTrue(test_pass)
def _state_tomography_quantum_program(self,
target,
state,
n_qubits,
shots=1):
qp = qiskit.QuantumProgram()
try:
backend = 'local_qiskit_simulator'
qp.get_backend_configuration(backend)
except LookupError:
backend = 'local_qasm_simulator'
# Prepared target state and assess quality
qp = self.target_prep(state, target, n_qubits, qp=qp)
prep_result = qp.execute(['prep'], backend=backend, shots=1)
prep_state = prep_result.get_data('prep')['quantum_state']
F_prep = state_fidelity(prep_state, target)
print('Prepared state fidelity =', F_prep)
# Run state tomography simulation and fit data to reconstruct circuit
qp, tomo_set, tomo_circuits = self.add_tomo_circuits(qp)
tomo_result = qp.execute(tomo_circuits, backend=backend, shots=shots)
tomo_data = tomo.tomography_data(tomo_result, 'prep', tomo_set)
rho_fit = tomo.fit_tomography_data(tomo_data)
# calculate fidelity and purity of fitted state
F_fit = state_fidelity(rho_fit, target)
pur = purity(rho_fit)
print('Fitted state fidelity =', F_fit)
print('Fitted state purity =', str(pur))
def state_tomography(state, n_qubits, shots):
# cat target state: [1. 0. 0. ... 0. 0. 1.]/sqrt(2.)
if state == 'cat':
target = np.zeros(pow(2, n_qubits))
target[0] = 1
target[pow(2, n_qubits)-1] = 1.0
target /= np.sqrt(2.0)
# random target state: first column of a random unitary
elif state == 'random':
target = random_unitary_matrix(pow(2, n_qubits))[0]
else:
raise QISKitError("Unknown state for tomography.")
print("target: {}".format(target))
# Use the local qasm simulator
backend = 'local_qasm_simulator'
qp = QuantumProgram()
# Prepared target state and assess quality
qp = target_prep(qp, state, target)
prep_result = qp.execute(['prep'], backend='local_statevector_simulator')
prep_state = prep_result.get_data('prep')['statevector']
F_prep = state_fidelity(prep_state, target)
print('Prepared state fidelity =', F_prep)
# Run state tomography simulation and fit data to reconstruct circuit
qp, tomo_set, tomo_circuits = add_tomo_circuits(qp)
tomo_result = qp.execute(tomo_circuits, backend=backend, shots=shots)
tomo_data = tomo.tomography_data(tomo_result, 'prep', tomo_set)
rho_fit = tomo.fit_tomography_data(tomo_data)
# calculate fidelity and purity of fitted state
F_fit = state_fidelity(rho_fit, target)
pur = purity(rho_fit)
print('Fitted state fidelity =', F_fit)
print('Fitted state purity =', str(pur))
return qp
def state_tomography(state, n_qubits, shots):
# cat target state: [1. 0. 0. ... 0. 0. 1.]/sqrt(2.)
if state == 'cat':
target = np.zeros(pow(2, n_qubits))
target[0] = 1
target[pow(2, n_qubits)-1] = 1.0
target /= np.sqrt(2.0)
# random target state: first column of a random unitary
elif state == 'random':
target = random_unitary_matrix(pow(2, n_qubits))[0]
else:
raise QISKitError("Unknown state for tomography.")
print("target: {}".format(target))
# Use the local qasm simulator
backend = 'local_qasm_simulator'
qp = QuantumProgram()
# Prepared target state and assess quality
qp = target_prep(qp, state, target)
prep_result = qp.execute(['prep'], backend='local_statevector_simulator')
prep_state = prep_result.get_data('prep')['statevector']
F_prep = state_fidelity(prep_state, target)
print('Prepared state fidelity =', F_prep)
# Run state tomography simulation and fit data to reconstruct circuit
qp, tomo_set, tomo_circuits = add_tomo_circuits(qp)
tomo_result = qp.execute(tomo_circuits, backend=backend, shots=shots)
tomo_data = tomo.tomography_data(tomo_result, 'prep', tomo_set)
rho_fit = tomo.fit_tomography_data(tomo_data)
# calculate fidelity and purity of fitted state
F_fit = state_fidelity(rho_fit, target)
pur = purity(rho_fit)
print('Fitted state fidelity =', F_fit)
print('Fitted state purity =', str(pur))
return qp
def _state_tomography(self, target, state, n_qubits, shots=1):
# Use the local qasm simulator
backend = qiskit.BasicAer.get_backend('statevector_simulator')
# Prepared target state and assess quality
prep_circ = self.target_prep(state, target, n_qubits)
prep_result = qiskit.execute(prep_circ, backend=backend).result()
prep_state = prep_result.get_statevector(prep_circ)
F_prep = state_fidelity(prep_state, target)
print('Prepared state fidelity =', F_prep)
# Run state tomography simulation and fit data to reconstruct circuit
tomo_set, tomo_circuits = self.add_tomo_circuits(prep_circ)
tomo_result = qiskit.execute(tomo_circuits,
backend=backend,
shots=shots).result()
tomo_data = tomo.tomography_data(tomo_result, prep_circ.name, tomo_set)
rho_fit = tomo.fit_tomography_data(tomo_data)
# calculate fidelity and purity of fitted state
F_fit = state_fidelity(rho_fit, target)
pur = purity(rho_fit)
print('Fitted state fidelity =', F_fit)
print('Fitted state purity =', str(pur))
def test_purity_1d_input(self):
input_state = [1, 0]
res = purity(input_state)
self.assertEqual(1, res)
print('Created State tomography circuits:')
for name in bell_tomo_circuit_names:
print(name)
# Use the local simulator# Use t
backend = 'local_qasm_simulator'
# Take 5000 shots for each measurement basis
shots = 5000
# Run the simulation
bell_tomo_result = Q_program.execute(bell_tomo_circuit_names,
backend=backend,
shots=shots)
print(bell_tomo_result)
bell_tomo_data = tomo.tomography_data(bell_tomo_result, 'bell', bell_tomo_set)
rho_fit = tomo.fit_tomography_data(bell_tomo_data)
# calculate fidelity, concurrence and purity of fitted state# calcul
F_fit = state_fidelity(rho_fit, bell_psi)
con = concurrence(rho_fit)
pur = purity(rho_fit)
# plot
plot_state(rho_fit, 'paulivec')
plot_state(rho_fit, 'city')
print('Fidelity =', F_fit)
print('concurrence = ', str(con))
print('purity = ', str(pur))
