Slow dynamics and magnon bound states in the 2D long-range quantum Ising model
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Quantum Physics arXiv:2512.09037 (quant-ph) [Submitted on 9 Dec 2025] Title:Slow dynamics and magnon bound states in the 2D long-range quantum Ising model Authors:Vighnesh Dattatraya Naik, Markus Heyl View a PDF of the paper titled Slow dynamics and magnon bound states in the 2D long-range quantum Ising model, by Vighnesh Dattatraya Naik and 1 other authors View PDF HTML (experimental) Abstract:The dynamics of long-range quantum Ising models represents a current frontier in experimental physics, notably in trapped ions or Rydberg atomic systems. However, a theoretical description of these dynamics beyond 1D remains a significant challenge for conventional methods. Here, we address this challenge by means of neural quantum states to simulate global quenches from the fully polarized ferromagnetic state in the 2D quantum Ising model with power-law decaying interactions. From these numerically exact simulations, we find that the dynamics exhibit slow relaxation with long-lived oscillations. We explain this behavior through a theory for the formation of magnon bound states, which are generated, as we show, through effective attractive interactions between magnons that persist over several lattice sites due to the power-law nature of the interactions. Our results are readily observable in current quantum simulation platforms realizing long-range interacting models such as in Rydberg atomic systems. Subjects: Quantum Physics (quant-ph); Statistical Mechanics (cond-mat.stat-mech) Cite as: arXiv:2512.09037 [quant-ph] (or arXiv:2512.09037v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2512.09037 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Vighnesh Dattatraya Naik [view email] [v1] Tue, 9 Dec 2025 19:00:06 UTC (2,579 KB) Full-text links: Access Paper: View a PDF of the paper titled Slow dynamics and magnon bound states in the 2D long-range quantum Ising model, by Vighnesh Dattatraya Naik and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2025-12 Change to browse by: cond-mat cond-mat.stat-mech 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?)