Speaker
Description
GPU-accelerated high-performance computing (HPC) provides an efficient way to increase the scale of numerical simulations of quantum circuits and quantum devices. In particular, this applies to quantum circuit simulation techniques based on tensor network formalism. In practice, there are two main challenges when building scalable HPC quantum circuit/device simulators based on tensor networks. First is scalable composability, that is, a simulator design based on expressive scalable data structures and algorithmic primitives capable of parallelization. Second is performance, in particular efficient execution on GPU accelerators. To address both challenges, we have integrated the tensor network processing library ExaTN with the cuTensorNet library from the cuQuantum package, where the former library provides expressivity, composability, and parallelism support that are necessary for implementing HPC quantum simulation methods based on tensor network formalism, while the latter library delivers high performance in contracting tensor networks on NVIDIA GPUs. We describe the capabilities of the ExaTN + cuTensorNet bundle and show its utility in implementing advanced quantum circuit simulation approaches.
