PIRSA:21110038

Video URL

https://pirsa.org/21110038

Contracting Arbitrary Tensor Networks: Approximate and Exact Approach with Applications in Graphical Models and Quantum Circuit Simulations

Pan, F. (2021). Contracting Arbitrary Tensor Networks: Approximate and Exact Approach with Applications in Graphical Models and Quantum Circuit Simulations. Perimeter Institute for Theoretical Physics. https://pirsa.org/21110038

Pan, Feng. Contracting Arbitrary Tensor Networks: Approximate and Exact Approach with Applications in Graphical Models and Quantum Circuit Simulations. Perimeter Institute for Theoretical Physics, Nov. 22, 2021, https://pirsa.org/21110038 

          @misc{ scivideos_PIRSA:21110038,
            doi = {10.48660/21110038},
            url = {https://pirsa.org/21110038},
            author = {Pan, Feng},
            keywords = {Quantum Information},
            language = {en},
            title = {Contracting Arbitrary Tensor Networks: Approximate and Exact Approach with Applications in Graphical Models and Quantum Circuit Simulations},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2021},
            month = {nov},
            note = {PIRSA:21110038 see, \url{https://scivideos.org/index.php/pirsa/21110038}}
          }

Feng Pan

Talk numberPIRSA:21110038
DOI10.48660/21110038
Source RepositoryPIRSA
Collection
Talk Type Scientific Series
Subject

Abstract

Tensor network algorithms are numerical tools widely used in physical research. But traditionally they are only applied to lattice systems with specific structure. In this talk, tensor network algorithms to deal with physical systems with arbitrary topology will be discussed. Theoretical framework will firstly be constructed to analyze the difficulty of contracting an arbitrary tensor network. Then both approximate and exact contraction approaches will be involved according to computational tasks of interest. Finally two applications, one in graphical models and the other in quantum circuit simulations, will be introduced to demonstrate the performance and potential of arbitrary tensor network algorithms.