Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative
Abstract Entanglement entropy (EE) plays a central role in the intersection of quantum information science and condensed matter physics. However, scanning the EE for two-dimensional and higher-dimensional systems still remains challenging. To address this challenge, we propose a quantum Monte Carlo...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-07-01
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| Series: | Nature Communications |
| Online Access: | https://doi.org/10.1038/s41467-025-61084-7 |
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| author | Zhe Wang Zhiyan Wang Yi-Ming Ding Bin-Bin Mao Zheng Yan |
| author_facet | Zhe Wang Zhiyan Wang Yi-Ming Ding Bin-Bin Mao Zheng Yan |
| author_sort | Zhe Wang |
| collection | DOAJ |
| description | Abstract Entanglement entropy (EE) plays a central role in the intersection of quantum information science and condensed matter physics. However, scanning the EE for two-dimensional and higher-dimensional systems still remains challenging. To address this challenge, we propose a quantum Monte Carlo scheme capable of extracting large-scale data of Rényi EE with high precision and low technical barrier. Its advantages lie in the following aspects: a single simulation can obtain the continuous variation curve of EE with respect to parameters, greatly reducing the computational cost; the algorithm implementation is simplified, and there is no need to alter the spacetime manifold during the simulation, making the code easily extendable to various many-body models. Additionally, we introduce a formula to calculate the derivative of EE without resorting to numerical differentiation from dense EE data. We then demonstrate the feasibility of using EE and its derivative to find phase transition points, critical exponents, and various phases. |
| format | Article |
| id | doaj-art-4aa846c64ffa460fac6edafdf6a2ea6f |
| institution | Kabale University |
| issn | 2041-1723 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Nature Communications |
| spelling | doaj-art-4aa846c64ffa460fac6edafdf6a2ea6f2025-08-20T04:01:34ZengNature PortfolioNature Communications2041-17232025-07-011611810.1038/s41467-025-61084-7Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivativeZhe Wang0Zhiyan Wang1Yi-Ming Ding2Bin-Bin Mao3Zheng Yan4Department of Physics, School of Science and Research Center for Industries of the Future, Westlake UniversityDepartment of Physics, School of Science and Research Center for Industries of the Future, Westlake UniversityDepartment of Physics, School of Science and Research Center for Industries of the Future, Westlake UniversitySchool of Foundational Education, University of Health and Rehabilitation SciencesDepartment of Physics, School of Science and Research Center for Industries of the Future, Westlake UniversityAbstract Entanglement entropy (EE) plays a central role in the intersection of quantum information science and condensed matter physics. However, scanning the EE for two-dimensional and higher-dimensional systems still remains challenging. To address this challenge, we propose a quantum Monte Carlo scheme capable of extracting large-scale data of Rényi EE with high precision and low technical barrier. Its advantages lie in the following aspects: a single simulation can obtain the continuous variation curve of EE with respect to parameters, greatly reducing the computational cost; the algorithm implementation is simplified, and there is no need to alter the spacetime manifold during the simulation, making the code easily extendable to various many-body models. Additionally, we introduce a formula to calculate the derivative of EE without resorting to numerical differentiation from dense EE data. We then demonstrate the feasibility of using EE and its derivative to find phase transition points, critical exponents, and various phases.https://doi.org/10.1038/s41467-025-61084-7 |
| spellingShingle | Zhe Wang Zhiyan Wang Yi-Ming Ding Bin-Bin Mao Zheng Yan Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative Nature Communications |
| title | Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative |
| title_full | Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative |
| title_fullStr | Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative |
| title_full_unstemmed | Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative |
| title_short | Bipartite reweight-annealing algorithm of quantum Monte Carlo to extract large-scale data of entanglement entropy and its derivative |
| title_sort | bipartite reweight annealing algorithm of quantum monte carlo to extract large scale data of entanglement entropy and its derivative |
| url | https://doi.org/10.1038/s41467-025-61084-7 |
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