{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/accurate-simulation-of-the-hubbard-model-with","title":"Accurate Simulation of the Hubbard Model with Finite Fermionic Projected Entangled Pair States","arxiv_id":"2502.13454","date":"2025-02-19","proceeding":null,"authors":["Wen-Yuan Liu","Huanchen Zhai","Ruojing Peng","Zheng-Cheng Gu","Garnet Kin-Lic Chan"],"abstract":"We demonstrate the use of finite-size fermionic projected entangled pair states, in conjunction with variational Monte Carlo, to perform accurate simulations of the ground-state of the 2D Hubbard model. Using bond dimensions of up to $D=28$, we show that we can surpass state-of-the-art DMRG energies that use up to $m=32000$ SU(2) multiplets on 8-leg ladders. We further apply our methodology to $10\\times 16$, $12\\times 16$ and $16 \\times 16$ lattices at $1/8$ hole doping and observe the dimensional crossover between stripe orientations. Our work shows the power of finite-size fermionic tensor networks to resolve the physics of the 2D Hubbard model and related problems.","url_abs":"https://arxiv.org/abs/2502.13454v2","url_pdf":"https://arxiv.org/pdf/2502.13454v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"links_only","authors_date_abstract":"arXiv metadata, CC0 1.0 (https://info.arxiv.org/help/license), from the Kaggle arXiv metadata snapshot of 2026-09-12"},"code_links":[{"paper_slug":"accurate-simulation-of-the-hubbard-model-with","repo_url":"https://github.com/hczhai/2d-hubbard-dmrg-2024","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}