Projected density matrix embedding theory with applications to the two-dimensional Hubbard model

Abstract

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 DMET (p-DMET), which achieves self-consistency without needing to optimize the correlation potential. We demonstrate the performance of p-DMET on the two-dimensional Hubbard model.

Document Details

Document Type
Pub Defense Publication
Publication Date
Aug 09, 2019
Source ID
10.1063/1.5108818

Entities

People

  • Garnet Chan
  • Lin Lin
  • Michael Lindsey
  • Xiaojie Wu
  • Yu Tong
  • Zhi-Hao Cui

Organizations

  • Air Force Office of Scientific Research
  • California Institute of Technology
  • Lawrence Berkeley National Laboratory
  • National Science Foundation
  • United States Department of Energy

Tags

Fields of Study

  • Physics

Readers

  • Defense Acquisition Program Management
  • Neural Network Machine Learning.
  • Quantum spin resonance or Electron Paramagnetic Resonance spectroscopy.

Technology Areas

  • Quantum Computing