5 papers
Enhancing robustness and efficiency of density matrix embedding theory via semidefinite programming and local correlation potential fitting
Xiaojie Wu, Michael Lindsey, Tiangang Zhou +2
Density matrix embedding theory (DMET) is a powerful quantum embedding method for solving strongly correlated quantum systems. Theoretically, the performance of a quantum embedding…
Near-optimal ground state preparation
Lin Lin, Yu Tong
Preparing the ground state of a given Hamiltonian and estimating its ground energy are important but computationally hard tasks. However, given some additional information, these p…
Optimal polynomial based quantum eigenstate filtering with application to solving quantum linear systems
Lin Lin, Yu Tong
We present a quantum eigenstate filtering algorithm based on quantum signal processing (QSP) and minimax polynomials. The algorithm allows us to efficiently prepare a target eigens…
Low-rank representation of tensor network operators with long-range pairwise interactions
Lin Lin, Yu Tong
Tensor network operators, such as the matrix product operator (MPO) and the projected entangled-pair operator (PEPO), can provide efficient representation of certain linear operato…
Projected Density Matrix Embedding Theory with Applications to the Two-Dimensional Hubbard Model
Xiaojie Wu, Zhi-Hao Cui, Yu Tong +3
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 o…