Density matrix embedding theory (DMET) is a quantum embedding theory for strongly correlated systems. From a computational perspective, one bottleneck in DMET is the optimization of the correlation potential to achieve self-consistency, especially for heterogeneous systems of large size. We propose a new method, called projected density matrix embedding theory (p-DMET), which achieves self-consistency without needing to optimize a correlation potential. We demonstrate the performance of p-DMET on the two-dimensional Hubbard model.
@article{arxiv.1905.00886,
title = {Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model},
author = {Xiaojie Wu and Zhi-Hao Cui and Yu Tong and Michael Lindsey and Garnet Kin-Lic Chan and Lin Lin},
journal= {arXiv preprint arXiv:1905.00886},
year = {2019}
}