Speaker
Description
A brief overview of recent advances in tensor network state (TNS) methods are presented that have the potential to broaden their scope of application radically for strongly correlated quantum many body systems. Novel mathematical models for hybrid multiNode-multiGPU parallelization on high-performance computing (HPC) infrastructures will be discussed. Scaling analysis on NVIDIA DGX-A100 and DXG-H100 platforms reaching quarter petaflops performance on a single node will be presented. We also report cutting-edge performance results via mixed precision ab initio Density Matrix Renormalization Group (DMRG) electronic structure calculations, adapted for state-of-the-art NVIDIA Blackwell technology, utilizing the Ozaki scheme for emulating FP64 arithmetic through the use of fixed-point compute resources. Finally, we showcase recent results obtained on IBM superconducting quantum processor with up to 144 qubits, together with classical validation, using state-of-the-art tensor network simulations via a novel Basis Update Galerkin (BUG) method, establishing agreement between quantum and classical approaches. We close our presentation discussing future possibilities via utilization of Blackwell NVL72 technology in tree-like TNS calculations opening new research directions in material sciences and beyond.
