FOR-QAOA: Fully Optimized Resource-Efficient QAOA Circuit Simulation for Solving the Max-Cut Problems
Chiu, Shinwei Yang, Chuo Min Hou, Shanjung Huang, Pohsuan Wang, Chuanchi Tu, Chiaheng Hung, Shihhao
Chiu, Shinwei
Yang, Chuo Min
Hou, Shanjung
Huang, Pohsuan
Wang, Chuanchi
Tu, Chiaheng
Hung, Shihhao
Supervisor
Department
Computer Science
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
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Research Projects
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Abstract
Quantum Approximate Optimization Algorithm (QAOA) is a promising hybrid quantum-classical method for solving combinatorial problems, but poses major simulation challenges on classical hardware. We present FOR-QAOA (Fully Optimized Resource-Efficient QAOA Circuit Simulation), a high-performance simulator designed for large-scale runs on both multi-node CPUs and multi-device GPUs. By leveraging techniques like cache-resident state operations and state vector transposition, FOR-QAOA achieves substantial speedups—up to 1490x on GPUs and 24x on CPUs—over existing simulators. It also scales efficiently across distributed environments, offering a powerful tool for advancing QAOA research on classical systems.
Citation
S.-W. Chiu et al., “FOR-QAOA: Fully Optimized Resource-Efficient QAOA Circuit Simulation for Solving the Max-Cut Problems,” Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration, pp. 1–11, Jul. 2025, doi: 10.1145/3708035.3736006
Source
Practice and Experience in Advanced Research Computing 2025
Conference
EARC '25: Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration
Keywords
Subjects
Source
EARC '25: Practice and Experience in Advanced Research Computing 2025: The Power of Collaboration
Publisher
Association for Computing Machinery