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arXiv · 2506.10531

GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems

Zhihao Xu·Srikar Chundury·Seongmin Kim·Amir Shehata·Xinyi Li·Ang Li·Tengfei Luo·Frank Mueller·In-Saeng Suh
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Abstract

Quantum computing holds great potential to accelerate the process of solving complex combinatorial optimization problems. The Distributed Quantum Approximate Optimization Algorithm (DQAOA) addresses high-dimensional, dense problems using current quantum computing techniques and high-performance computing (HPC) systems. In this work, we improve the scalability and efficiency of DQAOA through advanced problem decomposition and parallel execution using message passing on the Frontier CPU/GPU supercomputer. Our approach ensures efficient quantum-classical workload management by distributing large problem instances across classical and quantum resources. Experimental results demonstrate that enhanced decomposition strategies and GPU-accelerated quantum simulations significantly improve DQAOA's performance, achieving up to 10x speedup over CPU-based simulations. This advancement enables better scalability for large problem instances, supporting the practical deployment of GPU systems for hybrid quantum-classical applications. We also highlight ongoing integration efforts using the Quantum Framework (QFw) to support future HPC-quantum computing systems.

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Zhihao Xu, Srikar Chundury, Seongmin Kim, Amir Shehata, Xinyi Li, Ang Li, Tengfei Luo, Frank Mueller, In-Saeng Suh. 2025-06-12. GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems. https://arxiv.org/abs/2506.10531

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