You are here
Optimizing Tensor Network Ansatzes
This event has passed.
Friday, October 15, 2021 - 12:00pm to 1:00pm
Daniel Stilck França, Department of Mathematical Sciences, University of Copenhagen
Tensor network states form a variational ansatz class widely used, both analytically and numerically, in the study of quantum many-body systems. It is known that if the underlying graph contains a cycle, e.g., as in projected entangled pair states, then the set of tensor network states of given bond dimension is not closed. Its closure is the tensor network variety. Recent work has shown that states on the boundary of this variety can yield more efficient representations for states of physical interest, but it remained unclear how to systematically find and optimize over such representations. We address this issue by defining an ansatz class of states that includes states at the boundary of the tensor network variety of given bond dimension. We show how to optimize over this class in order to find ground states of local Hamiltonians by only slightly modifying standard algorithms and code for tensor networks. We apply this method to different models and observe favorable energies and runtimes when compared with standard tensor network methods. ---Co-sponsor: IBM Quantum Hub at NC State. Click "More Event Information" to pre-register for virtual seminar.