  Quantum computing is a growing field where the information is processed by
two-levels quantum states known as qubits. Current physical realizations of
qubits require a careful calibration, composed by different experiments, due to
noise and decoherence phenomena. Among the different characterization
experiments, a crucial step is to develop a model to classify the measured
state by discriminating the ground state from the excited state. In this
proceedings we benchmark multiple classification techniques applied to real
quantum devices.

Quantum Physics;Machine Learning