DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint

Quantum Physics arXiv:2512.00699 (quant-ph) [Submitted on 30 Nov 2025] Title:DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint Authors:Chenyi Zhang, Tao Shang, Chao Guo, Ruohan He View a PDF of the paper titled DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint, by Chenyi Zhang and 3 other authors View PDF HTML (experimental) Abstract:Variational quantum circuits face a critical trade-off between privacy and trainability. High expressivity required for robust privacy induces exponentially large dynamical Lie algebras. This structure inevitably leads to barren plateaus. Conversely, trainable models restricted to polynomial-sized algebras remain transparent to algebraic attacks. To resolve this impasse, DyLoC is proposed. This dual-layer architecture employs an orthogonal decoupling strategy. Trainability is anchored to a polynomial-DLA ansatz while privacy is externalized to the input and output interfaces. Specifically, Truncated Chebyshev Graph Encoding (TCGE) is employed to thwart snapshot inversion.

Dynamic Local Scrambling (DLS) is utilized to obfuscate gradients. Experiments demonstrate that DyLoC maintains baseline-level convergence with a final loss of 0.186. It outperforms the baseline by increasing the gradient reconstruction error by 13 orders of magnitude. Furthermore, snapshot inversion attacks are blocked when the reconstruction mean squared error exceeds 2.0. These results confirm that DyLoC effectively establishes a verifiable pathway for secure and trainable quantum machine learning. Subjects: Quantum Physics (quant-ph); Cryptography and Security (cs.CR) Cite as: arXiv:2512.00699 [quant-ph] (or arXiv:2512.00699v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.00699 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Chenyi Zhang [view email] [v1] Sun, 30 Nov 2025 02:29:13 UTC (1,154 KB) Full-text links: Access Paper: View a PDF of the paper titled DyLoC: A Dual-Layer Architecture for Secure and Trainable Quantum Machine Learning Under Polynomial-DLA constraint, by Chenyi Zhang and 3 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cs cs.CR 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?)