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A Framework for Integrating Quantum Simulation and High Performance Computing

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arxiv 2408.08098 v1 pith:NJ2MDUMJ submitted 2024-08-15 cs.DC quant-ph

classification cs.DCquant-ph
keywords quantumsimulationcomputingresourcesframeworksoftwarecontextdifferent
verification ladder T0 review T1 audit T2 compute T3 formal
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Scientific applications are starting to explore the viability of quantum computing. This exploration typically begins with quantum simulations that can run on existing classical platforms, albeit without the performance advantages of real quantum resources. In the context of high-performance computing (HPC), the incorporation of simulation software can often take advantage of the powerful resources to help scale-up the simulation size. The configuration, installation and operation of these quantum simulation packages on HPC resources can often be rather daunting and increases friction for experimentation by scientific application developers. We describe a framework to help streamline access to quantum simulation software running on HPC resources. This includes an interface for circuit-based quantum computing tasks, as well as the necessary resource management infrastructure to make effective use of the underlying HPC resources. The primary contributions of this work include a classification of different usage models for quantum simulation in an HPC context, a review of the software architecture for our approach and a detailed description of the prototype implementation to experiment with these ideas using two different simulators (TNQVM \& NWQ-Sim). We include initial experimental results running on the Frontier supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) using a synthetic workload generated via the SupermarQ quantum benchmarking framework.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems

    cs.DC 2025-06 conditional novelty 4.0 of 10

    GPU-accelerated DQAOA with impact-factor based decomposition runs up to 10x faster than CPU simulations on Frontier, with better scaling up to 160 devices.

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