Data di Pubblicazione:
2023
Citazione:
Benchmarking machine learning models for quantum state classification / E. Pedicillo, A. Pasquale, S. Carrazza. - (2023 Sep 14). (Intervento presentato al 26. convegno International Conference on Computing in High Energy & Nuclear Physics tenutosi a Norfolk : 8-12 May nel 2023) [10.48550/arXiv.2309.07679].
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.
Tipologia IRIS:
24 - Pre-print
Keywords:
Quantum Physics; Quantum Physics; Computer Science - Learning
Elenco autori:
E. Pedicillo, A. Pasquale, S. Carrazza
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