ExaTN - A Scalable Exascale Math Library for Hierarchical Tensor Network Representations and Simulations in Quantum Many-Body Theory and Beyond

Tensor network theory has recently paved the path to efficient numerical simulations of two- and three-dimensional many-body Hamiltonians describing strongly correlated quantum particles, but it still requires efficient software infrastructure that scales well on leadership heterogeneous HPC systems. To address this need, we develop ExaTN: A scalable math library for processing hierarchical tensor representations. Our library enables the use of advanced hierarchical tensor network states capable of expressing local expectation values in strongly entangled quantum systems efficiently. ExaTN allows building arbitrarily complex tensor networks for which it exposes a set of high-level API functions which automate tensor optimization procedures. A highly modular design of ExaTN allows seamless switching of computational backends for the computer system of choice, from a laptop to a leadership GPU-accelerated HPC platform, like Summit. The internal task-based parallel runtime then assures a load-balanced execution of tensor processing workloads.