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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.21567 |
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| _version_ | 1866915267514204160 |
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| author | Fang, Yu Xue, Cheng Sun, Tai-Ping Xu, Xiao-Fan Zhuang, Xi-Ning Wang, Yun-Jie Ye, Chuang-Chao Ma, Teng-Yang Zhang, Jia-Xuan Liu, Huan-Yu Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping |
| author_facet | Fang, Yu Xue, Cheng Sun, Tai-Ping Xu, Xiao-Fan Zhuang, Xi-Ning Wang, Yun-Jie Ye, Chuang-Chao Ma, Teng-Yang Zhang, Jia-Xuan Liu, Huan-Yu Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping |
| contents | Quantum computing promises exponential acceleration for fluid flow simulations, yet the measurement overhead required to extract flow features from quantum-encoded flow field data fundamentally undermines this advantage--a critical challenge termed the ``output problem''. To address this, we propose an orthogonal-polynomial-based quantum reduced-order model (PolyQROM) that integrates orthogonal polynomial basis transformations with variational quantum circuits (VQCs). PolyQROM employs optimized polynomial-based quantum operations to compress flow field data into low-dimensional representations while preserving essential features, enabling efficient quantum or classical post-processing for tasks like reconstruction and classification. By leveraging the mathematical properties of orthogonal polynomials, the framework enhances circuit expressivity and stabilizes training compared to conventional hardware-efficient VQCs. Numerical experiments demonstrate PolyQROM's effectiveness in reconstructing flow fields with high fidelity and classifying flow patterns with accuracy surpassing classical methods and quantum benchmarks, all while reducing computational complexity and parameter counts. The work bridges quantum simulation outputs with practical fluid analysis, addressing the ``output problem'' through efficient reduced-order modeling tailored for quantum-encoded flow data, offering a scalable pathway to exploit quantum advantages in computational fluid dynamics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_21567 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | PolyQROM: Orthogonal-Polynomial-Based Quantum Reduced-Order Model for Flow Field Analysis Fang, Yu Xue, Cheng Sun, Tai-Ping Xu, Xiao-Fan Zhuang, Xi-Ning Wang, Yun-Jie Ye, Chuang-Chao Ma, Teng-Yang Zhang, Jia-Xuan Liu, Huan-Yu Wu, Yu-Chun Chen, Zhao-Yun Guo, Guo-Ping Quantum Physics Quantum computing promises exponential acceleration for fluid flow simulations, yet the measurement overhead required to extract flow features from quantum-encoded flow field data fundamentally undermines this advantage--a critical challenge termed the ``output problem''. To address this, we propose an orthogonal-polynomial-based quantum reduced-order model (PolyQROM) that integrates orthogonal polynomial basis transformations with variational quantum circuits (VQCs). PolyQROM employs optimized polynomial-based quantum operations to compress flow field data into low-dimensional representations while preserving essential features, enabling efficient quantum or classical post-processing for tasks like reconstruction and classification. By leveraging the mathematical properties of orthogonal polynomials, the framework enhances circuit expressivity and stabilizes training compared to conventional hardware-efficient VQCs. Numerical experiments demonstrate PolyQROM's effectiveness in reconstructing flow fields with high fidelity and classifying flow patterns with accuracy surpassing classical methods and quantum benchmarks, all while reducing computational complexity and parameter counts. The work bridges quantum simulation outputs with practical fluid analysis, addressing the ``output problem'' through efficient reduced-order modeling tailored for quantum-encoded flow data, offering a scalable pathway to exploit quantum advantages in computational fluid dynamics. |
| title | PolyQROM: Orthogonal-Polynomial-Based Quantum Reduced-Order Model for Flow Field Analysis |
| topic | Quantum Physics |
| url | https://arxiv.org/abs/2504.21567 |