Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging

Quantum Physics arXiv:2511.02051 (quant-ph) [Submitted on 3 Nov 2025] Title:Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging Authors:Daniel Alejandro Lopez, Oscar Montiel, Oscar Castillo, Miguel Lopez-Montiel View a PDF of the paper titled Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging, by Daniel Alejandro Lopez and 3 other authors View PDF Abstract:Continuous-variable (CV) quantum computing offers a promising framework for scalable quantum machine learning, leveraging optical systems with infinite-dimensional Hilbert spaces. While discrete-variable (DV) quantum neural networks have shown remarkable progress in various computer vision tasks, CV quantum models remain comparatively underexplored. In this work, we present a feasibility study of continuous-variable quantum neural networks (CV-QCNNs) applied to biomedical image classification. Utilizing photonic circuit simulation frameworks, we construct CV quantum circuits composed of Gaussian gates, such as displacement, squeezing, rotation, and beamsplitters to emulate convolutional behavior. Our experiments are conducted on the MedMNIST dataset collection, a set of annotated medical image benchmarks for multiple diagnostic tasks. We evaluate CV-QCNN's performance in terms of classification accuracy, model expressiveness, and resilience to Gaussian noise, comparing against classical CNNs and equivalent DV quantum circuits. This study aims to identify trade-offs between DV and CV paradigms for quantum-enhanced medical imaging. Our results highlight the potential of continuous-variable models and their viability for future computer-aided diagnosis systems. Comments: Subjects: Quantum Physics (quant-ph); Emerging Technologies (cs.ET); Medical Physics (physics.med-ph) Cite as: arXiv:2511.02051 [quant-ph] (or arXiv:2511.02051v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2511.02051 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Oscar Humberto Montiel [view email] [v1] Mon, 3 Nov 2025 20:35:47 UTC (9,405 KB) Full-text links: Access Paper: View a PDF of the paper titled Towards Continuous-variable Quantum Neural Networks for Biomedical Imaging, by Daniel Alejandro Lopez and 3 other authorsView PDFTeX Source view license Current browse context: quant-ph new | recent | 2025-11 Change to browse by: cs cs.ET physics physics.med-ph 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?)