Work

Recommender engine for continuous-time quantum Monte Carlo methods

Year: 2017

Type: article

Abstract: Recommender systems play an essential role in the modern business world. They recommend favorable items such as books, movies, and search queries to users based on their past preferences. Applying similar ideas and techniques to Monte Carlo simulations of physical systems boosts their efficiency without sacrificing accuracy. Exploiting the quantum to classical mapping inherent in the continuous-time quantum Monte Carlo methods, we construct a classical molecular gas model to reproduce the quantum distributions. We then utilize powerful molecular simulation techniques to propose efficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers.

Source: Physical review. E

DOI: 10.1103/physreve.95.031301

Authors: Li Huang, Yi-feng Yang, Lei Wang

Institutions: Science and Technology on Surface Physics and Chemistry Laboratory, Chinese Academy of Sciences, Collaborative Innovation Center of Quantum Matter, Institute of Physics, University of Chinese Academy of Sciences

Language: English

Topic: Quantum many-body systems

Subfield: Atomic and Molecular Physics, and Optics

Field: Physics and Astronomy

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Chinese Academy of Sciences, China Academy of Engineering Physics, Ministry of Science and Technology of the People's Republic of China

Awards: G130430484, G2196265650, G3188338339, G3300856530, G5093582234