_version_ 1866916865545076736
author 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
author_facet 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
contents 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.
format Preprint
id arxiv_https___arxiv_org_abs_2406_06063
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers
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
Computational Physics
Quantum Physics
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.
title Enabling Large-Scale and High-Precision Fluid Simulations on Near-Term Quantum Computers
topic Computational Physics
Quantum Physics
url https://arxiv.org/abs/2406.06063