Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim

TL;DR

This paper presents TN-Sim, a scalable tensor network simulation tool integrated into NWQ-Sim, leveraging high-performance computing to enable efficient quantum circuit simulations for classical quantum computing research.

Contribution

The paper introduces TN-Sim within NWQ-Sim, combining TAMM and ITensor frameworks for scalable tensor network simulations on HPC systems, including task-based parallelization for wide quantum circuits.

Findings

Successfully demonstrated on Perlmutter supercomputer

Supports both local and distributed HPC simulations

Potential for portability to other HPC clusters

Abstract

Large-scale tensor network simulations are crucial for developing robust complexity-theoretic bounds on classical quantum simulation, enabling circuit cutting approaches, and optimizing circuit compilation, all of which aid efficient quantum computation on limited quantum resources. Modern exascale high-performance computing platforms offer significant potential for advancing tensor network quantum circuit simulation capabilities. We implement TN-Sim, a tensor network simulator backend within the NWQ-Sim software package that utilizes the Tensor Algebra for Many-body Methods (TAMM) framework to support both distributed HPC-scale computations and local simulations with ITensor. To optimize the scale up in computation across multiple nodes we implement a task based parallelization scheme to demonstrate parallelized gate contraction for wide quantum circuits with many gates per layer. Through the integration of the TAMM framework with Matrix Product State (MPS) tensor network approaches, we deliver a simulation environment that can scale from local systems to HPC clusters. We demonstrate an MPS tensor network simulator running on the state-of-the-art Perlmutter (NVIDIA) supercomputer and discuss the potential portability of this software to HPC clusters such as Frontier (AMD) and Aurora (Intel). We also discuss future improvements including support for different tensor network topologies and enhanced computational efficiency.