Machine Learning Detects Quantum Information Masking, Achieving Higher Classification Accuracy in Qubit States

Quantum Physics arXiv:2510.12507 (quant-ph) [Submitted on 14 Oct 2025] Title:Detection of quantum information masking via machine learning Authors:Sheng-Ao Mao, Lin Zhang, Bo Li View a PDF of the paper titled Detection of quantum information masking via machine learning, by Sheng-Ao Mao and Lin Zhang and Bo Li View PDF HTML (experimental) Abstract:Recently, machine learning has been widely applied in the field of quantum information, notably in tasks such as entanglement detection, steering characterization, and nonlocality verification. However, few studies have focused on utilizing machine learning to detect quantum information masking. In this work, we investigate supervised machine learning for detecting quantum information masking in both pure and mixed qubit states. For pure qubit states, we randomly generate the corresponding density matrices and train an XGBoost model to detect quantum information masking. For mixed qubit states, we improve the XGBoost method by optimizing the selection of training samples. The experimental results demonstrate that our approach achieves higher classification accuracy. Furthermore, we analyze the area under the curve (AUC) of the receiver operating characteristic curve for this method, which further confirms its classification performance. Comments: Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2510.12507 [quant-ph] (or arXiv:2510.12507v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2510.12507 Focus to learn more arXiv-issued DOI via DataCite Journal reference: Laser Phys. 35 (2025) 105202 Related DOI: https://doi.org/10.1088/1555-6611/ae0df6 Focus to learn more DOI(s) linking to related resources Submission history From: Sheng-Ao Mao [view email] [v1] Tue, 14 Oct 2025 13:36:53 UTC (14,515 KB) Full-text links: Access Paper: View a PDF of the paper titled Detection of quantum information masking via machine learning, by Sheng-Ao Mao and Lin Zhang and Bo LiView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-10 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?)