Related papers: Benchmarking machine learning models for quantum state classification

Benchmarking machine learning models for quantum state classification

URL: http://arxiv.org/abs/2309.07679v1
Date: Thu, 14 Sep 2023 12:45:20 GMT
Title: Benchmarking machine learning models for quantum state classification
Authors: Edoardo Pedicillo, Andrea Pasquale and Stefano Carrazza
Abstract summary: We develop a model to classify the measured state by discriminating the ground state from the excited state. We benchmark multiple classification techniques applied to real quantum devices.
Score: 0.0
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
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.

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