End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise
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Quantum Physics arXiv:2605.10963 (quant-ph) [Submitted on 7 May 2026] Title:End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise Authors:Hyunho Cha, Wonjung Kim, Jungwoo Lee View a PDF of the paper titled End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise, by Hyunho Cha and 2 other authors View PDF HTML (experimental) Abstract:Recent advancements in quantum computing highlight the need for efficient encoding of classical data into quantum states to ensure robust quantum information processing. Traditional encoding schemes often impose impractical requirements about the knowledge of quantum states and lack adaptability to noisy quantum channels and broader tasks. To address these limitations, we propose a novel end-to-end learnable quantum transcoding scheme explicitly optimized for compactness and robustness in noisy quantum communication scenarios. Our approach integrates neural network-based data compression with Cholesky decomposition-based quantum encoding and bypasses full density matrix reconstruction. Through normalized quantum observables, our method enables efficient tomography and achieves high reconstruction and classification performance even under extreme noise conditions. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2605.10963 [quant-ph] (or arXiv:2605.10963v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2605.10963 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Hyunho Cha [view email] [v1] Thu, 7 May 2026 14:14:23 UTC (1,762 KB) Full-text links: Access Paper: View a PDF of the paper titled End-to-End Neural and Quantum Transcoding for Compressed Latent Representation under Channel Noise, by Hyunho Cha and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-05 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?)