Self-learning Monte Carlo method: Continuous-time algorithm
Self-learning Monte Carlo method: Continuous-time algorithm
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DOI：
10.1103/physrevb.96.161102
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发表时间：
2017-05
影响因子：
3.7
通讯作者：
Y. Nagai;Huitao Shen;Yang Qi;Junwei Liu;L. Fu
中科院分区：
文献类型：
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作者：
Y. Nagai;Huitao Shen;Yang Qi;Junwei Liu;L. Fu
自学习蒙特卡罗(SLMC)方法通过设计和训练一个有效的模型来提出有效的全局更新来加快蒙特卡罗模拟的速度。我们在SLMC框架下实现了量子杂质模型的连续时间量子蒙特卡罗算法。我们引入并训练了一种图生成函数(DGF)来模拟所有阶图展开的连续虚时间中场组态的概率分布。通过使用DGF提出全局更新，我们证明了自学习的连续时间蒙特卡罗方法可以显著降低模拟的计算复杂度。
The self-learning Monte Carlo (SLMC) method speeds up the Monte Carlo simulation by designing and training an effective model to propose efficient global updates. We implement the continuous-time quantum Monte Carlo algorithm for quantum impurity models in the framework of SLMC. We introduce and train a diagram generating function (DGF) to model the probability distribution of field configurations in the continuous imaginary time at all orders of diagrammatic expansion. By using the DGF to propose global updates, we show that the self-learning continuous-time Monte Carlo method can significantly reduce the computational complexity of the simulation.