Abstract
An acceleration of continuous time quantum Monte Carlo (CTQMC) methods is a potentially interesting branch of work as they are matchless as impurity solvers of a dynamical mean field theory (DMFT) approach for the description of strongly correlated systems. The inversion of the matrix with operations given by the diagram expansion order in the CTQMC fast update and the multiplication of the matrix, and the noninteracting properties with operations to measure the -point correlators, are computationally time consuming. Here we propose the CTQMC method in combination with a machine learning technique, which eliminates the and operations for the two-point impurity Green's functions and four-point vertices , respectively. This method not only predicts the accurate physical properties at low temperature, but also dramatically decreases the computational times of for the nonlocal extension of DMFT approximation.
- Received 17 January 2019
- Revised 1 May 2019
DOI:https://doi.org/10.1103/PhysRevB.100.045153
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