Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim
Summarize this article with:
Quantum Physics arXiv:2601.04422 (quant-ph) [Submitted on 7 Jan 2026] Title:Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim Authors:Aaron C. Hoyt, Jonathan S. Bersson, Sean Garner, Chenxu Liu, Ang Li View a PDF of the paper titled Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim, by Aaron C. Hoyt and 4 other authors View PDF HTML (experimental) 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. Subjects: Quantum Physics (quant-ph) Cite as: arXiv:2601.04422 [quant-ph] (or arXiv:2601.04422v1 [quant-ph] for this version) https://doi.org/10.48550/arXiv.2601.04422 Focus to learn more arXiv-issued DOI via DataCite (pending registration) Journal reference: Proc. 2025 IEEE High Performance Extreme Computing Conf. (HPEC), 2025, pp. 1-7 Related DOI: https://doi.org/10.1109/HPEC67600.2025.11196314 Focus to learn more DOI(s) linking to related resources Submission history From: Aaron Hoyt [view email] [v1] Wed, 7 Jan 2026 22:01:35 UTC (243 KB) Full-text links: Access Paper: View a PDF of the paper titled Implementation of Tensor Network Simulation TN-Sim under NWQ-Sim, by Aaron C. Hoyt and 4 other authorsView PDFHTML (experimental)TeX Source view license Current browse context: quant-ph new | recent | 2026-01 References & Citations INSPIRE HEP NASA ADSGoogle Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) Links to Code Toggle Papers with Code (What is Papers with Code?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)