Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Gopal Ramesh Dahale"'
Autor:
Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 5, p 323 (2024)
We introduce a hybrid quantum-classical vision transformer architecture, notable for its integration of variational quantum circuits within both the attention mechanism and the multi-layer perceptrons. The research addresses the critical challenge of
Externí odkaz:
https://doaj.org/article/ba7655e110f347c69ac7cffb2b367b8c
Autor:
Zhongtian Dong, Marçal Comajoan Cara, Gopal Ramesh Dahale, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva, Eyup B. Unlu
Publikováno v:
Axioms, Vol 13, Iss 3, p 188 (2024)
This paper presents a comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNNs) and Quantum Neural Networks (QNNs), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENNs) and Deep Neural Netwo
Externí odkaz:
https://doaj.org/article/0192a5e664424331a27f91b6f3667e0a
Autor:
Eyup B. Unlu, Marçal Comajoan Cara, Gopal Ramesh Dahale, Zhongtian Dong, Roy T. Forestano, Sergei Gleyzer, Daniel Justice, Kyoungchul Kong, Tom Magorsch, Konstantin T. Matchev, Katia Matcheva
Publikováno v:
Axioms, Vol 13, Iss 3, p 187 (2024)
Models based on vision transformer architectures are considered state-of-the-art when it comes to image classification tasks. However, they require extensive computational resources both for training and deployment. The problem is exacerbated as the
Externí odkaz:
https://doaj.org/article/ce457c3cbddd493ca8ef48f9f78124d9
Autor:
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
Publikováno v:
Axioms, Vol 13, Iss 3, p 160 (2024)
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 structu
Externí odkaz:
https://doaj.org/article/e9db4f8727244fc289812cfefef0ace7