| Article | |
| Report number | arXiv:2309.07679 ; TIF-UNIMI-2023-20 |
| Title | Benchmarking machine learning models for quantum state classification |
| Author(s) | Pedicillo, Edoardo (Milan U. ; INFN, Milan ; Technol. Innovation Inst., UAE) ; Pasquale, Andrea (Milan U. ; INFN, Milan ; Technol. Innovation Inst., UAE) ; Carrazza, Stefano (Milan U. ; INFN, Milan ; Technol. Innovation Inst., UAE ; CERN) |
| Publication | 2024 |
| Imprint | 2023-09-14 |
| Number of pages | 9 |
| In: | EPJ Web Conf. 295 (2024) 12007 |
| In: | 26th International Conference on Computing in High Energy & Nuclear Physics, Norfolk, Virginia, Us, 8 - 12 May 2023, pp.12007 |
| DOI | 10.1051/epjconf/202429512007 |
| Subject category | cs.LG ; Computing and Computers ; quant-ph ; General Theoretical Physics |
| Abstract | 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. |
| Copyright/License | preprint: (License: arXiv nonexclusive-distrib 1.0) publication: © 2024 The Authors (License: CC-BY-4.0) |
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Record created 2023-12-13, last modified 2024-05-22