Recommender Engine for Continuous Time Quantum Monte Carlo Methods

School of Physical Sciences, University of Chinese Academy of Sciences, Beijing 100190, China Recommender systems play an essential role in the modern business world. They recommend favorable items like 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 e fficiency 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 e fficient quantum Monte Carlo updates. The recommender engine approach provides a general way to speed up the quantum impurity solvers. At the heart of every quantum Monte Carlo (QMC) method is a quantum to classical mapping.