Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers
| Title: | Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers |
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| Authors: | Chen, Zhao-Yun, Ma, Teng-Yang, Ye, Chuang-Chao, Xu, Liang, Tan, Ming-Yang, Zhuang, Xi-Ning, Xu, Xiao-Fan, Wang, Yun-Jie, Sun, Tai-Ping, Chen, Yong, Du, Lei, Guo, Liang-Liang, Zhang, Hai-Feng, Tao, Hao-Ran, Wang, Tian-Le, Yang, Xiao-Yan, Zhao, Ze-An, Wang, Peng, Zhang, Sheng, Zhang, Chi, Zhao, Ren-Ze, Jia, Zhi-Long, Kong, Wei-Cheng, Dou, Meng-Han, Wang, Jun-Chao, Liu, Huan-Yu, Xue, Cheng, Zhang, Peng-Jun-Yi, Huang, Sheng-Hong, Duan, Peng, Wu, Yu-Chun, Guo, Guo-Ping |
| Publication Year: | 2024 |
| Collection: | Physics (Other) Quantum Physics |
| Subject Terms: | Physics - Computational Physics, Quantum Physics |
| More Details: | Quantum computational fluid dynamics (QCFD) offers a promising alternative to classical computational fluid dynamics (CFD) by leveraging quantum algorithms for higher efficiency. This paper introduces a comprehensive QCFD method, including an iterative method "Iterative-QLS" that suppresses error in quantum linear solver, and a subspace method to scale the solution to a larger size. We implement our method on a superconducting quantum computer, demonstrating successful simulations of steady Poiseuille flow and unsteady acoustic wave propagation. The Poiseuille flow simulation achieved a relative error of less than $0.2\%$, and the unsteady acoustic wave simulation solved a 5043-dimensional matrix. We emphasize the utilization of the quantum-classical hybrid approach in applications of near-term quantum computers. By adapting to quantum hardware constraints and offering scalable solutions for large-scale CFD problems, our method paves the way for practical applications of near-term quantum computers in computational science. Comment: 31 pages, 10 figures |
| Document Type: | Working Paper |
| Access URL: | http://arxiv.org/abs/2406.06063 |
| Accession Number: | edsarx.2406.06063 |
| Database: | arXiv |
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