Myopic Entropy Scheduling for Ramsey Magnetometry

Quantum Physics arXiv:2510.21108 (quant-ph) [Submitted on 24 Oct 2025] Title:Myopic Entropy Scheduling for Ramsey Magnetometry Authors:Julian Greentree, William Moran, Rob Evans, Andrew Melatos, Neel Kanth Kundu, Peter M. Farrell View a PDF of the paper titled Myopic Entropy Scheduling for Ramsey Magnetometry, by Julian Greentree and 5 other authors View PDF HTML (experimental) Abstract:This paper presents an entropy based adaptive measurement sequence strategy for quantum sensing of magnetic fields. To physically ground our ideas we consider a sensor employing a nitrogen vacancy center in diamond, however our approach is applicable to other quantum sensor arrangements. The sensitivity and accuracy of these sensors typically rely on long sequences of rapidly occurring measurements. We introduce a new technique for designing these measurement sequences aimed at reducing the number of measurements required for a specified accuracy as measured by entropy, by selecting measurement parameters that optimally reduce entropy at each measurement. We compare, via simulation, the efficiency and sensitivity of our new method with several existing measurement sequence design strategies. Our results show quantifiable improvements in sensing performance. We also show analytically that our entropy reduction approach, reduces, under certain simplified conditions, to a well-known and widely used measurement strategy. Comments: Subjects: Quantum Physics (quant-ph); Mathematical Physics (math-ph) Cite as: arXiv:2510.21108 [quant-ph] (or arXiv:2510.21108v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2510.21108 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Julian Greentree [view email] [v1] Fri, 24 Oct 2025 02:56:28 UTC (1,637 KB) Full-text links: Access Paper: View a PDF of the paper titled Myopic Entropy Scheduling for Ramsey Magnetometry, by Julian Greentree and 5 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-10 Change to browse by: math math-ph math.MP 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?)