Paper ID: 2309.07679

Benchmarking machine learning models for quantum state classification

Edoardo Pedicillo, Andrea Pasquale, Stefano Carrazza

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.

Submitted: Sep 14, 2023