https://doi.org/10.1051/epjconf/202429512007
Benchmarking machine learning models for quantum state classification
1 TIF Lab, Dipartimento di Fisica, Università degli Studi di Milano and INFN Sezione di Milano, Milan, Italy.
2 Quantum Research Center, Technology Innovation Institute, Abu Dhabi, UAE.
3 CERN, Theoretical Physics Department, CH-1211 Geneva 23, Switzerland.
* e-mail: edoardo.pedicillo@unimi.it
** e-mail: andrea.pasquale@unimi.it
*** e-mail: stefano.carrazza@unimi.it
Published online: 6 May 2024
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.
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
