Speaker
Simone Montangero
(Padova University)
Description
We review some recent results on the development of efficient tree tensor network algorithms and their applications to quantum simulation benchmarking and theoretical interpretation. In particular, we present results on lattice gauge theories 2+1 and 3+1 dimensions at finite density, and out-of-equilibrium in 1+1 dimensions. Moreover, we present a roadmap for future tensor networks simulations of increasing complexity. Finally, we present the application of tensor network methods to the solution of hard classical combinatorial problems via mapping to many-body quantum hamiltonians and of tensor network machine learning for b-bbar tagging at LHCb.
