A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Benutzer: Gast

Dokumenttyp:: Zeitschriftenaufsatz
Autor(en):: Forestano, Roy T. ; Comajoan Cara, Marçal ; Dahale, Gopal Ramesh ; Dong, Zhongtian ; Gleyzer, Sergei ; Justice, Daniel ; Kong, Kyoungchul ; Magorsch, Tom ; Matchev, Konstantin T. ; Matcheva, Katia ; Unlu, Eyup B.
Titel:: A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks
Stichworte:: Article ; quantum computing ; deep learning ; quantum machine learning ; equivariance ; invariance ; supervised learning ; classification ; particle physics ; Large Hadron Collider ; 81P68 ; 68Q12
Zeitschriftentitel:: Axioms
Jahr:: 2024
Band / Volume:: 13
Heft / Issue:: 3
Volltext / DOI:: doi:10.3390/axioms13030160
Verlag / Institution:: MDPI
E-ISSN:: 2075-1680
Publikationsdatum:: 29.02.2024
CC-Lizenz:: by, https://creativecommons.org/licenses/by/4.0

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