Learning high-dimensional quantum entanglement through physics-guided neural networks
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Quantum Physics arXiv:2604.03482 (quant-ph) [Submitted on 3 Apr 2026] Title:Learning high-dimensional quantum entanglement through physics-guided neural networks Authors:Yang Xu, Hao Zhang, Wenwen Zhang, Luchang Niu, Girish Kulkarni, Mahtab Amooei, Sergio Carbajo, Robert W. Boyd View a PDF of the paper titled Learning high-dimensional quantum entanglement through physics-guided neural networks, by Yang Xu and Hao Zhang and Wenwen Zhang and Luchang Niu and Girish Kulkarni and Mahtab Amooei and Sergio Carbajo and Robert W. Boyd View PDF HTML (experimental) Abstract:High-gain spontaneous parametric down-conversion (SPDC) produces bright squeezed vacuum with rich high-dimensional entanglement, but its output is inherently multimodal and non-perturbative, making the full modal characterization a major computational bottleneck. We propose a physics-guided deep neural network that reconstructs the source's modal fingerprint: the high-dimensional correlation signature across radial and azimuthal indices. We designed a FiLM-modulated convolutional architecture that predicts the joint (m,l) distribution, and training is driven by a hybrid loss that couples data-driven metrics (JSD, KL, MSE, Wasserstein) with a soft orbital-angular-momentum (OAM) conservation term, providing an essential inductive bias toward physically consistent solutions. Across gain regimes, our method achieves high-fidelity reconstruction with average JSD of 1.96e-3, WEMD of 1.54e-3, and KL divergence of 7.85e-3, delivering an approximate 128-fold speedup over full numerical simulation and more than 30% accuracy gains over U-Net baselines. These results demonstrate that physics-guided learning, via a soft OAM-conservation regularizer and physically generated training targets, enables rapid and data-efficient modal characterization. Compared with traditional numerical simulation, our mesh-free method has demonstrated good generalization with limited or contaminated training data and has enabled fast "online" prediction of the quantum dynamics of a high-dimensional entanglement system for real-world experimental implementation. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2604.03482 [quant-ph] (or arXiv:2604.03482v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2604.03482 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Yang Xu [view email] [v1] Fri, 3 Apr 2026 22:09:21 UTC (15,684 KB) Full-text links: Access Paper: View a PDF of the paper titled Learning high-dimensional quantum entanglement through physics-guided neural networks, by Yang Xu and Hao Zhang and Wenwen Zhang and Luchang Niu and Girish Kulkarni and Mahtab Amooei and Sergio Carbajo and Robert W. BoydView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-04 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?)