Self-learning quantum Monte Carlo method in interacting fermion systems
Journal Article
·
· Physical Review B
- Chinese Academy of Sciences, Beijing (China). Beijing National Laboratory for Condensed Matter Physics and Institute of Physics; University of Chinese Academy of Sciences, Beijing (China). School of Physical Sciences; Massachusetts Institute of Technology
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Department of Physics
- Chinese Academy of Sciences, Beijing (China). Beijing National Laboratory for Condensed Matter Physics and Institute of Physics; University of Chinese Academy of Sciences, Beijing (China). School of Physical Sciences
We present the self-learning Monte Carlo method is a powerful general-purpose numerical method recently introduced to simulate many-body systems. In this work, we extend it to an interacting fermion quantum system in the framework of the widely used determinant quantum Monte Carlo. This method can generally reduce the computational complexity and moreover can greatly suppress the autocorrelation time near a critical point. This enables us to simulate an interacting fermion system on a $100 × 100$ lattice even at the critical point and obtain critical exponents with high precision.
- Research Organization:
- Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22). Materials Sciences & Engineering Division
- Grant/Contract Number:
- SC0010526
- OSTI ID:
- 1424921
- Alternate ID(s):
- OSTI ID: 1371802
- Journal Information:
- Physical Review B, Journal Name: Physical Review B Journal Issue: 4 Vol. 96; ISSN 2469-9950; ISSN PRBMDO
- Publisher:
- American Physical Society (APS)Copyright Statement
- Country of Publication:
- United States
- Language:
- English
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