End-to-end performance of quantum-accelerated large-scale linear algebra workflows

2026-03-16

AI Summary

This work addresses the computational bottleneck caused by fill-in effects in solving large-scale sparse linear systems by proposing a hybrid quantum-classical graph partitioning method. For the first time, it integrates an NISQ-era Iterative-QAOA quantum optimizer end-to-end into the industrial multiphysics simulation software LS-DYNA. Leveraging the CUDA-Q/cuTensorNet simulation platform and IonQ Forte quantum hardware, the approach executes via MPI-based distributed computing on AWS and Synopsys clusters, significantly accelerating finite element analyses such as modal and transient simulations. Evaluated on automotive and aircraft engine models with up to 35 million elements, the framework reduces overall solve time by an average of 7%, with peak improvements reaching 15%, demonstrating its scalable acceleration potential for both current NISQ devices and future fault-tolerant quantum computing architectures.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Sampling/Simulation-based Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs

Abstract

Solving large-scale sparse linear systems is a challenging computational task due to the introduction of non-zero elements, or"fill-in."The Graph Partitioning Problem (GPP) arises naturally when minimizing fill-in and accelerating solvers. In this paper, we measure the end-to-end performance of a hybrid quantum-classical framework designed to accelerate Finite Element Analysis (FEA) by integrating a quantum solver for GPP into Synopsys/Ansys'LS-DYNA multiphysics simulation software. The quantum solver we use is based on Iterative-QAOA, a scalable, non-variational quantum approach for optimization. We focus on two specific classes of FEA problems, namely vibrational (eigenmode) analysis and transient simulation. We report numerical simulations on up to 150 qubits done on NVIDIA's CUDA-Q/cuTensorNet and implementation on IonQ's Forte quantum hardware. The potential impact on LS-DYNA workflows is quantified by measuring the wall-clock time-to-solution for complex problem instances, including vibrational analysis of large finite element models of a sedan car and a Rolls-Royce jet engine, as well as transient simulations of a drill and an impeller. We performed end-to-end performance measurements on meshes comprising up to 35 million elements. Measurements were conducted using LS-DYNA in distributed-memory mode via Message Passing Interface (MPI) on AWS and Synopsys compute clusters. Our findings indicate that with a quantum computer in the loop, amortized LS-DYNA wall-clock time can be improved by up to 15% for specific cases and by at least 7% for all models considered. These results highlight the significant potential of quantum computing to reduce time-to-solution for large-scale FEA simulations within the Noisy Intermediate-Scale Quantum (NISQ) era, offering an approach that is scalable and extendable into the fault-tolerant quantum computing regime.

Problem

• large-scale sparse linear systems
• fill-in
• Graph Partitioning Problem
• Finite Element Analysis
• quantum-accelerated workflows

Innovation

• Iterative-QAOA
• Graph Partitioning Problem
• Finite Element Analysis
• Quantum-Classical Hybrid
• LS-DYNA