PIRSA:17080008

A tensor network framework for topological phases of quantum matter

APA

Sahinoglu, B. (2017). A tensor network framework for topological phases of quantum matter. Perimeter Institute for Theoretical Physics. https://pirsa.org/17080008

MLA

Sahinoglu, Burak. A tensor network framework for topological phases of quantum matter. Perimeter Institute for Theoretical Physics, Aug. 03, 2017, https://pirsa.org/17080008

BibTex

          @misc{ scivideos_PIRSA:17080008,
            doi = {10.48660/17080008},
            url = {https://pirsa.org/17080008},
            author = {Sahinoglu, Burak},
            keywords = {Quantum Foundations, Quantum Information},
            language = {en},
            title = {A tensor network framework for topological phases of quantum matter},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2017},
            month = {aug},
            note = {PIRSA:17080008 see, \url{https://scivideos.org/index.php/pirsa/17080008}}
          }
          

Burak Sahinoglu University of Vienna

Talk numberPIRSA:17080008

Abstract

We present a general scheme for constructing topological lattice models in any space dimension using tensor networks. Our approach relies on finding "simplex tensors" that satisfy a finite set of tensor equations. Given any such tensor, we construct a discrete topological quantum field theory (TQFT) and local commuting projector Hamiltonians on any lattice. The ground space degeneracy of these models is a topological invariant that can be computed via the TQFT, and the ground states are locally indistinguishable when the ground space is nondegenerate on the sphere. Any ground state can be realized by a tensor network obtained by contracting simplex tensors. Our models are exact renormalization fixed points, covering a broad range of models in the literature. We identify symmetries on the virtual level of the tensor networks of our models that generalize the topological invariance properties beyond fixed point models. This framework combined with recent tensor network techniques is convenient for studying excitations, their statistics, phase transitions, and ultimately for classification of gapped phases of many-body theories in 3+1 and higher dimensions.