Uploaded July 2022 | Updated September 2026, 1 hour ago
In this study, we evaluate the possibility of using quantum neuronal networks for classifying gravitational waveforms, ran both on simulators and quantum computers. This analysis is quite interdisciplinary in its nature, combining knowledge of astrophysics, quantum information or quantum and classical machine learning. We saw that the quantum classifiers and hybrid classical-quantum layers give high accurate results when tested on a simple dataset and ran on a simulator; also, adding a quantum layer to an unsatisfying classical neuronal network can highly improve its accuracy. When running on a real quantum computer, error minimizing algorithms need to be implemented in order to obtain a satisfying accuracy.
Authors: Maria-Catalina Isfan, Laurentiu-Ioan Caramete, Ana Caramete, Traian Popescu
Presenter: Maria-Catalina Isfan
In this study, we evaluate the possibility of using quantum neuronal networks for classifying gravitational waveforms, ran both on simulators and quantum computers. This analysis is quite interdisciplinary in its nature, combining knowledge of astrophysics, quantum information or quantum and classical machine learning. We saw that the quantum classifiers and hybrid classical-quantum layers give high accurate results when tested on a simple dataset and ran on a simulator; also, adding a quantum layer to an unsatisfying classical neuronal network can highly improve its accuracy. When running on a real quantum computer, error minimizing algorithms need to be implemented in order to obtain a satisfying accuracy.
Authors: Maria-Catalina Isfan, Laurentiu-Ioan Caramete, Ana Caramete, Traian Popescu
Presenter: Maria-Catalina Isfan
