Permutation matrix representation quantum Monte Carlo
Abstract
We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We showcase the flexibility of our algorithm and the advantages it offers over existing state-of-the-art by simulating transverse-field Ising model Hamiltonians and comparing the performance of our technique against that of the stochastic series expansion algorithm. We also study a transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions.
- Authors:
-
- University of Southern California, Los Angeles, CA (United States)
- University of New Mexico, Albuquerque, NM (United States)
- University of Southern California, Los Angeles, CA (United States); University of Southern California, Marina del Rey, CA (United States)
- Publication Date:
- Research Org.:
- Univ. of Southern California, Los Angeles, CA (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES); USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR); US Army Research Office (ARO)
- OSTI Identifier:
- 1656954
- Alternate Identifier(s):
- OSTI ID: 1830800
- Grant/Contract Number:
- SC0020280; W911NF-17-C-0050
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Journal of Statistical Mechanics
- Additional Journal Information:
- Journal Volume: 2020; Journal Issue: 7; Journal ID: ISSN 1742-5468
- Publisher:
- IOP Publishing
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND SUPERFLUIDITY
Citation Formats
Gupta, Lalit, Albash, Tameem, and Hen, Itay. Permutation matrix representation quantum Monte Carlo. United States: N. p., 2020.
Web. doi:10.1088/1742-5468/ab9e64.
Gupta, Lalit, Albash, Tameem, & Hen, Itay. Permutation matrix representation quantum Monte Carlo. United States. https://doi.org/10.1088/1742-5468/ab9e64
Gupta, Lalit, Albash, Tameem, and Hen, Itay. Tue .
"Permutation matrix representation quantum Monte Carlo". United States. https://doi.org/10.1088/1742-5468/ab9e64. https://www.osti.gov/servlets/purl/1656954.
@article{osti_1656954,
title = {Permutation matrix representation quantum Monte Carlo},
author = {Gupta, Lalit and Albash, Tameem and Hen, Itay},
abstractNote = {We present a quantum Monte Carlo algorithm for the simulation of general quantum and classical many-body models within a single unifying framework. The algorithm builds on a power series expansion of the quantum partition function in its off-diagonal terms and is both parameter-free and Trotter error-free. In our approach, the quantum dimension consists of products of elements of a permutation group. As such, it allows for the study of a very wide variety of models on an equal footing. To demonstrate the utility of our technique, we use it to clarify the emergence of the sign problem in the simulations of non-stoquastic physical models. We showcase the flexibility of our algorithm and the advantages it offers over existing state-of-the-art by simulating transverse-field Ising model Hamiltonians and comparing the performance of our technique against that of the stochastic series expansion algorithm. We also study a transverse-field Ising model augmented with randomly chosen two-body transverse-field interactions.},
doi = {10.1088/1742-5468/ab9e64},
journal = {Journal of Statistical Mechanics},
number = 7,
volume = 2020,
place = {United States},
year = {Tue Jul 28 00:00:00 EDT 2020},
month = {Tue Jul 28 00:00:00 EDT 2020}
}
Web of Science
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