PIRSA:23010114 Scientific Series Other

DOI10.48660/23010114

Series: Machine Learning Initiative

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}}
}

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

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