| Issue |
EPJ Web of Conf.
Volume 295, 2024
26th International Conference on Computing in High Energy and Nuclear Physics (CHEP 2023)
|
|
|---|---|---|
| Article Number | 12007 | |
| Number of page(s) | 9 | |
| Section | Quantum Computing | |
| DOI | https://doi.org/10.1051/epjconf/202429512007 | |
| Published online | 06 May 2024 | |
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
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