Extracting universal corner entanglement entropy during the quantum Monte Carlo simulation
The subleading corner logarithmic corrections in entanglement entropy (EE) are crucial for revealing the universal characteristics of quantum critical points (QCPs), but they are challenging to detect. Motivated by recent developments in the stable computation of EE in ($2+1$)-dimensional [($2+1$)D] quantum many-body systems, we have developed a method for directly measuring the corner contribution in EE with lower computational cost. The cornerstone of our approach is to measure the subtracted corner entanglement entropy (SCEE), defined as the difference between the EEs of subregions with the same boundary length for smooth and cornered boundaries during a sign-problem-free quantum Monte Carlo simulation. Our improved method inherently eliminates not only the area-law term of EE but also the subleading log corrections arising from Goldstone modes, leaving the universal corner contribution as the leading term of the SCEE with greatly improved data quality. Utilizing this advanced approach, we calculate the SCEE of the bilayer Heisenberg model on both square and honeycomb lattices across their ($2+1$)D $O$(3) QCPs with different opening angles on the entanglement boundary and obtain accurate values of the corresponding universal corner log coefficients. These findings will encourage further theoretical investigations to access controlled universal information for interacting conformal field theories at ($2+1$) dimensions.
Authors
- Zi Yang Meng (ORCID: https://orcid.org/0000-0001-9771-7494)
- Menghan Song (ORCID: https://orcid.org/0000-0002-4932-9961)
- Yuan Da Liao (ORCID: https://orcid.org/0000-0002-3018-4029)
- Jiarui Zhao
Institutions
- University of Hong Kong (HK)
Publication Details
- Journal
- Physical review. B./Physical review. B
- Published
- 2024-12-03
- DOI
- https://doi.org/10.1103/physrevb.110.235111
- Citations
- 5
- Primary Topic
- Quantum many-body systems
- Type
- article
- Field-Weighted Citation Impact
- 1.69
Funders
- Agence Nationale de la Recherche