GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems
Fuente:
arXiv
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| Autori principali: | , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866916791907778560 |
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| author | Xu, Zhihao Chundury, Srikar Kim, Seongmin Shehata, Amir Li, Xinyi Li, Ang Luo, Tengfei Mueller, Frank Suh, In-Saeng |
| author_facet | Xu, Zhihao Chundury, Srikar Kim, Seongmin Shehata, Amir Li, Xinyi Li, Ang Luo, Tengfei Mueller, Frank Suh, In-Saeng |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_10531 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems Xu, Zhihao Chundury, Srikar Kim, Seongmin Shehata, Amir Li, Xinyi Li, Ang Luo, Tengfei Mueller, Frank Suh, In-Saeng Distributed, Parallel, and Cluster Computing 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. |
| title | GPU-Accelerated Distributed QAOA on Large-scale HPC Ecosystems |
| topic | Distributed, Parallel, and Cluster Computing |
| url | https://arxiv.org/abs/2506.10531 |