PIRSA:23010114

Fermionic Gaussian Circuits for Tensor Networks: Application to the Single Impurity Anderson Model

Wu, A. (2023). Fermionic Gaussian Circuits for Tensor Networks: Application to the Single Impurity Anderson Model. Perimeter Institute. https://pirsa.org/23010114

Wu, Angkun. Fermionic Gaussian Circuits for Tensor Networks: Application to the Single Impurity Anderson Model. Perimeter Institute, Jan. 31, 2023, https://pirsa.org/23010114 

          @misc{ pirsa_PIRSA:23010114,
            doi = {10.48660/23010114},
            url = {https://pirsa.org/23010114},
            author = {Wu, Angkun},
            keywords = {Other},
            language = {en},
            title = {Fermionic Gaussian Circuits for Tensor Networks: Application to the Single Impurity Anderson Model},
            publisher = {Perimeter Institute},
            year = {2023},
            month = {jan},
            note = {PIRSA:23010114 see, \url{https://pirsa.org}}
          }

Angkun Wu

Rutgers University

Talk number
PIRSA:23010114
Collection
Talk Type
Subject
Abstract

We present an approach for representing fermionic quantum many-body states using tensor networks, by introducing a change of basis with local unitary gates obtained via compressing fermionic Gaussian states into quantum circuits. These fermionic Gaussian circuits enable efficient disentangling of low-energy states and entanglement renormalization in matrix product states/operators, significantly reducing bond dimension and improving computational efficiency. As a demonstration, we apply this approach to the 1D single impurity Anderson model through suppression of entanglement in both ground states and time-evolved low-lying excited states. We also explore the use of hierarchical compression to generate Gaussian multi-scale entanglement renormalization ansatz (GMERA) circuits and study their emergent coarse-grained physical models in terms of entanglement properties and suitability for time evolution.

Zoom link:  https://pitp.zoom.us/j/99677586832?pwd=ZGdIVGpac3pWcnhJYjlkNU16Wmlndz09