Entanglement and discord classification via deep learning
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Quantum Physics arXiv:2601.22253 (quant-ph) [Submitted on 29 Jan 2026] Title:Entanglement and discord classification via deep learning Authors:Katherine Muñoz-Mellado, Daniel Uzcátegui-Contreras, Antonio Guerra, Aldo Delgado, Dardo Goyeneche View a PDF of the paper titled Entanglement and discord classification via deep learning, by Katherine Mu\~noz-Mellado and Daniel Uzc\'ategui-Contreras and Antonio Guerra and Aldo Delgado and Dardo Goyeneche View PDF HTML (experimental) Abstract:In this work, we propose a deep learning-based approach for quantum entanglement and discord classification using convolutional autoencoders. We train models to distinguish entangled from separable bipartite states for $d \times d$ systems with local dimension $d$ ranging from two to seven, which enables identification of bound and free entanglement. Through extensive numerical simulations across various quantum state families, we demonstrate that our model achieves high classification accuracy. Furthermore, we leverage the learned representations to generate samples of bound entangled states, the rarest form of entanglement and notoriously difficult to construct analytically. We separately train the same convolutional autoencoders architecture for detecting the presence of quantum discord and show that the model also exhibits high accuracy while requiring significantly less training time. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.22253 [quant-ph] (or arXiv:2601.22253v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.22253 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Daniel Uzcategui [view email] [v1] Thu, 29 Jan 2026 19:23:42 UTC (6,516 KB) Full-text links: Access Paper: View a PDF of the paper titled Entanglement and discord classification via deep learning, by Katherine Mu\~noz-Mellado and Daniel Uzc\'ategui-Contreras and Antonio Guerra and Aldo Delgado and Dardo GoyenecheView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 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?)