{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/mathbb-z-2-times-mathbb-z-2-equivariant","title":"$\\mathbb{Z}_2\\times \\mathbb{Z}_2$ Equivariant Quantum Neural Networks: Benchmarking against Classical Neural Networks","arxiv_id":"2311.18744","date":"2023-11-30","proceeding":null,"authors":["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"],"abstract":"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.","url_abs":"https://arxiv.org/abs/2311.18744v3","url_pdf":"https://arxiv.org/pdf/2311.18744v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"mathbb-z-2-times-mathbb-z-2-equivariant","repo_url":"https://github.com/zhongtiand/eqnn","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"benchmarking","task_name":"Benchmarking"},{"task_slug":"binary-classification","task_name":"Binary Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}