Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks

Quantum Physics arXiv:2512.03094 (quant-ph) [Submitted on 1 Dec 2025] Title:Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks Authors:Tomás Villalba-Ferreiro, Eduardo Mosqueira-Rey, Diego Alvarez-Estevez View a PDF of the paper titled Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks, by Tom\'as Villalba-Ferreiro and Eduardo Mosqueira-Rey and Diego Alvarez-Estevez View PDF HTML (experimental) Abstract:This study explores the performance of Quantum Support Vector Classifiers (QSVCs) and Quantum Neural Networks (QNNs) in comparison to classical models for machine learning tasks. By evaluating these models on the Iris and MNIST-PCA datasets, we find that quantum models tend to outperform classical approaches as the problem complexity increases. While QSVCs generally provide more consistent results, QNNs exhibit superior performance in higher-complexity tasks due to their increased quantum load. Additionally, we analyze the impact of hyperparameter tuning, showing that feature maps and ansatz configurations significantly influence model accuracy. We also compare the PennyLane and Qiskit frameworks, concluding that Qiskit provides better optimization and efficiency for our implementation. These findings highlight the potential of Quantum Machine Learning (QML) for complex classification problems and provide insights into model selection and optimization strategies Comments: Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2512.03094 [quant-ph] (or arXiv:2512.03094v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.03094 Focus to learn more arXiv-issued DOI via DataCite Journal reference: 2025 IEEE International Joint Conference on Neural Networks (IJCNN 2025) - poster track Submission history From: Diego Alvarez-Estevez [view email] [v1] Mon, 1 Dec 2025 09:36:57 UTC (292 KB) Full-text links: Access Paper: View a PDF of the paper titled Performance Analysis of Quantum Support Vector Classifiers and Quantum Neural Networks, by Tom\'as Villalba-Ferreiro and Eduardo Mosqueira-Rey and Diego Alvarez-EstevezView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cs cs.LG References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)