Venue & metadata

• Journal/Proceedings: International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings
• Pages: 509 – 516
• Note: Cited by: 0; All Open Access, Hybrid Gold Open Access
• Author keywords: Face Recognition; Labelled Faces in the Wild; Quantum Convolutional Neural Networks

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.

Keywords

Convolutional neural networks GS Face recognition GS Image classification GS Quantum channel GS Quantum optics GS Academic community GS Central point GS Convolutional neural network GS Images classification GS Industrial communities GS Labeled face in the wild GS Machine-learning GS Quantum Computing GS Quantum convolutional neural network GS Quantum machines GS Qubits GS

Links & artifacts

DOI Publisher Copy BibTeX Download .bib

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Suggested citation

Napoli, F., Campanile, L., De Gregorio, G., & Marrone, S. (2025). Quantum Convolutional Neural Networks for Image Classification: Perspectives and Challenges [Conference paper]. International Conference on Internet of Things, Big Data and Security, IoTBDS - Proceedings, 509–516. https://doi.org/10.5220/0013521500003944