22070028

From Neural density Operators to Tensor Networks

APA

Vincentini, F. (2022). From Neural density Operators to Tensor Networks. Perimeter Institute for Theoretical Physics. http://pirsa.org/22070028

MLA

Vincentini, Filippo. From Neural density Operators to Tensor Networks. Perimeter Institute for Theoretical Physics, Jul. 22, 2022, http://pirsa.org/22070028

BibTex

          @misc{ scitalks_22070028,
            doi = {},
            url = {http://pirsa.org/22070028},
            author = {Vincentini, Filippo},
            keywords = {Quantum Matter},
            language = {en},
            title = {From Neural density Operators to Tensor Networks},
            publisher = {Perimeter Institute for Theoretical Physics},
            year = {2022},
            month = {jul},
            note = {Talk #22070028 see, \url{https://scitalks.ca}}
          }
          

Filippo Vincentini L'Ecole Polytechnique Federale de Lausanne (EPFL)

Source Repository PIRSA
Collection
Talk Type Scientific Series

Abstract

In this talk I will discuss recent advances in Neural-Network parametrisations of pure and mixed quantum states that can be efficiently sampled.  In the first part I will generalise the Neural Density Operator ansatz originally proposed by Torlai and Melko by combining two general ingredients that can be used to construct deep, autoregressive ansatze that automatically enforce positive definitness.  In the second part, instead, I will show that any matrix product state can be exactly represented by a recurrent neural network with a linear memory update. I will then discuss how to generalise this linear RNN architecture to 2D lattices, comparing both approaches to standard DMRG calculations.

Zoom link:  https://pitp.zoom.us/j/98833163484?pwd=bEZTeTB0c1l1QmkrQXdEc3dBQ0dJZz09