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

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

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as gr...

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Bibliographic Details
Main Authors: Roy T. Forestano, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Format: Article
Language:English
Published: MDPI AG 2024-02-01
Series:Axioms
Subjects:
quantum computing
deep learning
quantum machine learning
equivariance
invariance
supervised learning
Online Access:https://www.mdpi.com/2075-1680/13/3/160

Internet

https://www.mdpi.com/2075-1680/13/3/160

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