DOI Publisher Details Copy BibTeX Download .bib
{"key"=>"Napoli2025509", "type"=>"Conference paper", "bibtex"=>"@conference{Napoli2025509,\n author = {Napoli, Fabio and Campanile, Lelio and De Gregorio, Giovanni and Marrone, Stefano},\n title = {Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges},\n year = {2025},\n journal = {International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings},\n pages = {509 – 516},\n doi = {10.5220/0013521500003944}\n}\n", "author"=>"Napoli, Fabio and Campanile, Lelio and De Gregorio, Giovanni and Marrone, Stefano", "author_array"=>[{"first"=>"Fabio", "last"=>"Napoli", "prefix"=>"", "suffix"=>""}, {"first"=>"Lelio", "last"=>"Campanile", "prefix"=>"", "suffix"=>""}, {"first"=>"Giovanni", "last"=>"De Gregorio", "prefix"=>"", "suffix"=>""}, {"first"=>"Stefano", "last"=>"Marrone", "prefix"=>"", "suffix"=>""}], "author_0_first"=>"Fabio", "author_0_last"=>"Napoli", "author_0_prefix"=>"", "author_0_suffix"=>"", "author_1_first"=>"Lelio", "author_1_last"=>"Campanile", "author_1_prefix"=>"", "author_1_suffix"=>"", "author_2_first"=>"Giovanni", "author_2_last"=>"De Gregorio", "author_2_prefix"=>"", "author_2_suffix"=>"", "author_3_first"=>"Stefano", "author_3_last"=>"Marrone", "author_3_prefix"=>"", "author_3_suffix"=>"", "title"=>"Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges", "year"=>"2025", "journal"=>"International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings", "pages"=>"509 – 516", "doi"=>"10.5220/0013521500003944", "url"=>"https://www.scopus.com/inward/record.uri?eid=2-s2.0-105003709151&doi=10.5220%2f0013521500003944&partnerID=40&md5=a87c421d81b24f527357b7b3f212464c", "abstract"=>"Quantum Computing is becoming a central point of discussion in both academic and industrial communities. Quantum Machine Learning is one of the most promising subfields of this technology, in particular for image classification. In this paper, the model of Quantum Convolutional Neural Networks and some related implementations are explored in their potential for a non-trivial task of image classification. The paper presents some experimentations and discusses the limitations and the strengths of these approaches when compared with classical Convolutional Neural Networks. Furthermore, an analysis of the impact of the noise level on the quality of the classification task has been performed. This paper reports a substantial equivalence of the perfomance of the model with respect the level of noise. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.", "author_keywords"=>"Face Recognition; Labelled Faces in the Wild; Quantum Convolutional Neural Networks", "keywords"=>"Convolutional neural networks; Face recognition; Image classification; Quantum channel; Quantum optics; Academic community; Central point; Convolutional neural network; Images classification; Industrial communities; Labeled face in the wild; Machine-learning; Quantum Computing; Quantum convolutional neural network; Quantum machines; Qubits", "publication_stage"=>"Final", "source"=>"Scopus", "note"=>"Cited by: 0; All Open Access, Hybrid Gold Open Access"}