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• W2575679290 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." @default.
• W2575679290 created "2017-01-26" @default.
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• W2575679290 date "2017-03-29" @default.
• W2575679290 modified "2023-10-16" @default.
• W2575679290 title "Recommender engine for continuous-time quantum Monte Carlo methods" @default.
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• W2575679290 doi "https://doi.org/10.1103/physreve.95.031301" @default.
• W2575679290 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/28415198" @default.
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