End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks

Quantum Physics arXiv:2609.25044 (quant-ph) [Submitted on 30 Aug 2026] Title:End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks Authors:Melek Krichen, Nikhitha Nunavath, Riccardo Bassoli, Soumaya Cherkaoui View a PDF of the paper titled End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks, by Melek Krichen and 3 other authors View PDF HTML (experimental) Abstract:This paper presents a quantum semantic communication (QSemCom) framework combining quantum machine learning (QML) and semantic communication (SemCom). Classical data are compressed into low-dimensional semantic representations, encoded and processed by a variational quantum transmitter, transmitted through a quantum channel, and processed by a trainable quantum receiver for classification. The framework considers a distributed quantum communication scenario in which quantum processing units (QPUs) exchange task-relevant semantic information through quantum links. While the general setting may involve multiple quantum nodes, this work focuses on the fundamental two-node case, with transmitter and receiver QPUs connected through a noisy quantum channel. Using MNIST, the framework is evaluated under ideal, bit-flip, depolarizing, and amplitude-damping channels. A baseline model is first trained over a perfect channel and evaluated under increasing noise without retraining. Receiver-side end-to-end training is then performed at fixed depolarizing-noise levels. The perfect-channel model achieves an accuracy of $0.9556$ and an F1-score of $0.9551$. Results show channel-dependent performance degradation, while receiver training substantially restores task performance under moderate and high depolarizing noise. Moreover, task recovery does not require reconstruction of the transmitted density matrix, highlighting a distinction between physical-state recovery and semantic-feature recovery. These results demonstrate that a trainable quantum receiver can recover task-relevant semantic information from noise-distorted quantum states and maintain high classification performance. Subjects: Quantum Physics (quant-ph); Machine Learning (cs.LG) Cite as: arXiv:2609.25044 [quant-ph] (or arXiv:2609.25044v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2609.25044 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Melek Krichen [view email] [v1] Sun, 30 Aug 2026 21:31:32 UTC (3,986 KB) Full-text links: Access Paper: View a PDF of the paper titled End-to-End Quantum Semantic Communication with Variational Quantum Neural Networks, by Melek Krichen and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-09 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?) 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?)