$\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866911806416486400 |
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| author | Dong, Zhongtian Cara, Marçal Comajoan Dahale, Gopal Ramesh Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia Unlu, Eyup B. |
| author_facet | Dong, Zhongtian Cara, Marçal Comajoan Dahale, Gopal Ramesh Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia Unlu, Eyup B. |
| contents | This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_18744 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | $\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks Dong, Zhongtian Cara, Marçal Comajoan Dahale, Gopal Ramesh Forestano, Roy T. Gleyzer, Sergei Justice, Daniel Kong, Kyoungchul Magorsch, Tom Matchev, Konstantin T. Matcheva, Katia Unlu, Eyup B. Quantum Physics Machine Learning High Energy Physics - Phenomenology This paper presents a comprehensive comparative analysis of the performance of Equivariant Quantum Neural Networks (EQNN) and Quantum Neural Networks (QNN), juxtaposed against their classical counterparts: Equivariant Neural Networks (ENN) and Deep Neural Networks (DNN). We evaluate the performance of each network with two toy examples for a binary classification task, focusing on model complexity (measured by the number of parameters) and the size of the training data set. Our results show that the $\mathbb{Z}_2\times \mathbb{Z}_2$ EQNN and the QNN provide superior performance for smaller parameter sets and modest training data samples. |
| title | $\mathbb{Z}_2\times \mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks |
| topic | Quantum Physics Machine Learning High Energy Physics - Phenomenology |
| url | https://arxiv.org/abs/2311.18744 |