A non-linear quantum neural network framework for entanglement engineering
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Quantum Physics arXiv:2512.13971 (quant-ph) [Submitted on 16 Dec 2025] Title:A non-linear quantum neural network framework for entanglement engineering Authors:Adriano Macarone-Palmieri, Alberto Ferrara, Rosario Lo Franco View a PDF of the paper titled A non-linear quantum neural network framework for entanglement engineering, by Adriano Macarone-Palmieri and 2 other authors View PDF HTML (experimental) Abstract:Multipartite entanglement is a key resource for quantum technologies, yet its scalable generation in noisy quantum devices remains challenging. Here, we propose a low-depth quantum neural network architecture with linear scaling, inspired by memory-enabled photonic components, for variational entanglement engineering. The network incorporates physically motivated non-linear activation functions, enhancing expressivity beyond linear variational circuits at fixed depth.
By Monte Carlo sampling over circuit topologies, we identify architectures that efficiently generate highly entangled pure states, approaching the GHz limit, and demonstrate a clear advantage of non-linear networks up to 20 qubits. For the noisy scenario, we employ the experimentally accessible Meyer-Wallach global entanglement as a surrogate optimization cost and certify entanglement using bipartite negativity. For mixed states of up to ten qubits, the optimized circuits generate substantial entanglement across both symmetric and asymmetric bipartitions. These results establish an experimentally motivated and scalable variational framework for engineering multipartite entanglement on near-term quantum devices, highlighting the combined role of non-linearity and circuit topology. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2512.13971 [quant-ph] (or arXiv:2512.13971v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.13971 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Adriano Macarone-Palmieri [view email] [v1] Tue, 16 Dec 2025 00:16:51 UTC (1,050 KB) Full-text links: Access Paper: View a PDF of the paper titled A non-linear quantum neural network framework for entanglement engineering, by Adriano Macarone-Palmieri and 2 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 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?)