Fast classical simulation of `Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor'

Quantum Physics arXiv:2608.13805 (quant-ph) [Submitted on 13 Aug 2026] Title:Fast classical simulation of `Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor' Authors:Xiao-Yu Ouyang, Runze Chi, Garnet Kin-Lic Chan View a PDF of the paper titled Fast classical simulation of `Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor', by Xiao-Yu Ouyang and 1 other authors View PDF HTML (experimental) Abstract:We study the Néel quench dynamics of a 1D Fermi-Hubbard model which has recently been simulated on quantum hardware. We demonstrate that the set of 7260 observable trajectories measured in the quantum experiment can be obtained more quickly and accurately through classical tensor network simulation using modest computation. Our result relies on transverse tensor network contraction, where a bond dimension of 32 is already sufficient to reproduce the quantum experiment. We further extend the converged observable trajectories to longer times than in the hardware simulation and in other recent classical simulations. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2608.13805 [quant-ph] (or arXiv:2608.13805v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2608.13805 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Submission history From: Xiao-Yu Ouyang [view email] [v1] Thu, 13 Aug 2026 22:31:30 UTC (1,382 KB) Full-text links: Access Paper: View a PDF of the paper titled Fast classical simulation of `Fast, accurate, high-resolution simulation of large-scale Fermi-Hubbard models on a digital quantum processor', by Xiao-Yu Ouyang and 1 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-08 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?) 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?)