# Process tomography of a Hadamard gate
q = QuantumRegister(1)
circ = QuantumCircuit(q)
circ.h(q[0])

# Run circuit on unitary simulator to find ideal unitary
job = qiskit.execute(circ, Aer.get_backend('unitary_simulator'))
ideal_unitary = job.result().get_unitary(circ)
# convert to Choi-matrix in column-major convention
choi_ideal = outer(ideal_unitary.ravel(order='F'))

# Generate process tomography circuits and run on qasm simulator
qpt_circs = process_tomography_circuits(circ, q)
job = qiskit.execute(qpt_circs, Aer.get_backend('qasm_simulator'), shots=4000)

# Extract tomography data so that counts are indexed by measurement configuration
qpt_tomo = ProcessTomographyFitter(job.result(), qpt_circs)
qpt_tomo.data

choi_lstsq = qpt_tomo.fit(method='lstsq')

print('fit fidelity (state):', state_fidelity(choi_ideal / 2, choi_lstsq.data / 2))
print('fit fidelity (process):', np.real(process_fidelity(choi_ideal, choi_lstsq.data, require_cptp=False)))

fit fidelity (state): 0.9976767994222256
fit fidelity (process): 0.995358994837865