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Intell."],"published-print":{"date-parts":[[2025,6]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>This paper investigates the optimization properties of hybrid quantum-classical quanvolutional neural networks (QuanvNNs), i.e., hybrid architectures merging quanvolutional layers with classical dense layers. We explore several optimization strategies by assessing 9 optimizers across various configurations of layers and kernel size. Through rigorous experimentation, it was found that optimizers such as Adabelief, RMSProp, and Adam outperform others, showcasing high accuracy scores. Conversely, Fromage, SM3, and Adagrad exhibit notably lower performance, highlighting the difficulty of effectively navigating the loss landscape. These findings emphasize the critical role of optimizer selection in a gradient-based variational optimization strategy. Additionally, quanvolutional layer parameters exhibit minimal fluctuations during the training phase, while classical parameters undergo variations on an order of magnitude higher. This disparity suggests a distinct behavior in the optimization dynamics between quantum and classical components, potentially influencing the efficacy of each layer type and offering valuable insights into the underlying mechanisms governing quanvolutional filters. We also evaluate QuanvNN robustness through comparisons against classical CNNs in scenarios involving few-shot learning and input perturbations. Our experimental findings underscore not only enhanced convergence capabilities of QuanvNNs, but also emphasize their superior efficacy in handling severely perturbed inputs. Finally, a randomized QuanvNN variant is tested, yielding comparable performance to optimized models as suggested by previous experiments on parameters variation. All these findings underscore the potential of QuanvNNs in offering robustness and optimization potential compared to their classical counterparts, with implications for quantum-enhanced machine learning.<\/jats:p>","DOI":"10.1007\/s42484-025-00241-z","type":"journal-article","created":{"date-parts":[[2025,2,10]],"date-time":"2025-02-10T05:29:41Z","timestamp":1739165381000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["On hybrid quanvolutional neural networks optimization"],"prefix":"10.1007","volume":"7","author":[{"given":"Andrea","family":"Ceschini","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrea","family":"Carbone","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alessandro","family":"Sebastianelli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Massimo","family":"Panella","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bertrand","family":"Le Saux","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,2,10]]},"reference":[{"key":"241_CR1","doi-asserted-by":"crossref","unstructured":"Abbas A, Sutter D, Zoufal C, Lucchi A, Figalli A, Woerner S (2021) The power of quantum neural networks. 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