Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR

[2412.09557] Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR Skip to main content Search Submit Donate Log in Search arXiv Press Enter to search · Advanced search Quantum Physics arXiv:2412.09557 (quant-ph) [Submitted on 12 Dec 2024 ( v1 ), last revised 15 Aug 2026 (this version, v3)] Title: Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR Authors: Vivek Sabarad , Vishal Varma , T. S. Mahesh View a PDF of the paper titled Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR, by Vivek Sabarad and 2 other authors View PDF HTML (experimental) Abstract: Quantum kernel learning (QKL) promises efficient machine learning by encoding feature maps onto exponentially large Hilbert spaces inherent in quantum systems. Using the liquid-state nuclear magnetic resonance (NMR) platform, we implement and benchmark QKL for one-dimensional regression and two-dimensional classification tasks. We then classify entangling and non-entangling operators by extending QKL to handle parametrized or non-parameterized operator inputs. We first compute the kernel numerically for a double-layered star system and then experimentally validate it on a 3-qubit NMR register. QKL provides a practical route to compare operators on native quantum hardware without expensive tomography protocols. Our results confirm the superiority of QKL over other classical methods for processing quantum data, thereby highlighting its ability to capture the inherent structure of quantum space and to extend its domain of operation beyond the training domain by exploiting symmetries in the operator space. Comments: 11 pages, 6 figures Subjects: Quantum Physics (quant-ph) ; Machine Learning (cs.LG); Applied Physics (physics.app-ph) Cite as: arXiv:2412.09557 [quant-ph] (or arXiv:2412.09557v3 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2412.09557 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Vivek Sabarad [ view email ] [v1] Thu, 12 Dec 2024 18:44:38 UTC (2,034 KB) [v2] Tue, 15 Apr 2025 19:51:53 UTC (4,343 KB) [v3] Sat, 15 Aug 2026 12:22:12 UTC (3,021 KB) Full-text links: Access Paper: View a PDF of the paper titled Experimentally Extending Quantum Kernel Learning to Quantum Data by NMR, by Vivek Sabarad and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: quant-ph < prev | next > new | recent | 2024-12 Change to browse by: cs cs.LG physics physics.app-ph References & Citations INSPIRE HEP NASA ADS Google 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? ) We gratefully acknowledge support from our major funders , member institutions , , and all contributors. About · Help · Contact · Subscribe · Copyright · Privacy · Accessibility · Operational Status (opens in new tab) Major funding support from

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