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Trapped Ion Quantum Computing
Quantum Machine Learning
Benchmarking machine learning models for quantum state classification
arXiv
Authors: Edoardo Pedicillo, Andrea Pasquale, Stefano Carrazza
Year
2023
Paper ID
54854
Status
Preprint
Abstract Read
~2 min
Abstract Words
77
Citations
N/A
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.
Why This Paper Matters
- This paper contributes to the Quantum Machine Learning research area in the Quantum Articles archive.
- It adds a 2023 reference point for readers tracking recent quantum research.
- Quantum computing is a growing field where the information is processed by two-levels quantum states known as qubits.
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