Journal article
Improved coarse-graining methods for two dimensional tensor networks including fermions
The journal of high energy physics, Vol.2023(1), pp.24-22
01/09/2023
DOI: 10.1007/JHEP01(2023)024
Abstract
We show how to apply renormalization group algorithms incorporating entanglement filtering methods and a loop optimization to a tensor network which includes Grassmann variables which represent fermions in an underlying lattice field theory. As a numerical test a variety of quantities are calculated for two dimensional Wilson-Majorana fermions and for the two flavor Gross-Neveu model. The improved algorithms show much better accuracy for quantities such as the free energy and the determination of Fisher's zeros.
Details
- Title: Subtitle
- Improved coarse-graining methods for two dimensional tensor networks including fermions
- Creators
- Muhammad Asaduzzaman - Syracuse UniversitySimon Catterall - Syracuse UniversityYannick Meurice - University of IowaRyo Sakai - Syracuse UniversityGoksu Can Toga - Syracuse University
- Resource Type
- Journal article
- Publication Details
- The journal of high energy physics, Vol.2023(1), pp.24-22
- DOI
- 10.1007/JHEP01(2023)024
- ISSN
- 1029-8479
- eISSN
- 1029-8479
- Publisher
- Springer Nature
- Number of pages
- 22
- Grant note
- DE-AC02-05CH11231 / National Energy Research Scientific Computing Center (NERSC), a U.S. Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory HEP-ERCAP0020659 / NERSC DE-SC0019139 / U.S. Department of Energy (DOE); United States Department of Energy (DOE) ACI-1341006 / NSF; National Science Foundation (NSF)
- Language
- English
- Date published
- 01/09/2023
- Academic Unit
- Physics and Astronomy
- Record Identifier
- 9984429042802771
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