Speaker
Description
Recently we have proposed combination of the valence-space in-medium similarity renormalization group (VS-IMSRG) with the density matrix renormalization group (DMRG) offering a scalable and flexible many-body approach for strongly correlated open-shell nuclei. Combined with an analysis of quantum information measures, this further establishes the VS-DMRG as a valuable method for ab initio calculations of nuclei. Here we briefly overview recent advances in tensor network state methods that have the potential to further boost the application of DMRG in nuclear physics. These include a general approach to find an optimal representation of a quantum many body wave function, i.e., a parametrization with the minimum number of parameters for a given error margin via global fermionic mode optimization, combination of DMRG with restricted active space method (DMRG-RAS-X), multi-orbital correlations and entanglement, developments on hybrid CPU-multiGPU parallelization, and an efficient treatment of non-Abelian symmetries on high performance computing (HPC) infrastructures. Scaling analysis on NVIDIA DGX-H100 platform is also presented, advertising that quarter petaflops performance can be reached on a single node.
