From Neural density Operators to Tensor Networks


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


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


          @misc{ pirsa_22070028,
            doi = {},
            url = {https://pirsa.org/22070028},
            author = {Vincentini, Filippo},
            keywords = {Condensed Matter},
            language = {en},
            title = {From Neural density Operators to Tensor Networks},
            publisher = {Perimeter Institute},
            year = {2022},
            month = {jul},
            note = {PIRSA:22070028 see, \url{https://pirsa.org}}

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

Talk Type Scientific Series


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