SAPO: Improving the Scalability and Accuracy of Quantum Linear Solver for Portfolio Optimization
Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentall...
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| Vydáno v: | 2025 62nd ACM/IEEE Design Automation Conference (DAC) s. 1 - 7 |
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IEEE
22.06.2025
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| Abstract | Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentally limited by either poor scalability or insufficient accuracy. In this paper, we propose SAPO, which formally articulates the quantum circuit that seamlessly integrates financial theory and historical data characteristics with quantum algebra. The circuit design is extended from the HHL algorithm incorporating mean-variance theory, which promotes scalability by equivalent transformation. Then, we present a min-max eigenvalue model that leverages historical financial information to refine parameter settings with high accuracy. Experiments conducted on market data demonstrate that SAPO can effectively reduce the complexity by \mathbf{3 6. 9 4 \%} compared to basic HHL [1], [2] and improve the accuracy by 1.46 \times compared to hybrid HHL [3]. |
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| AbstractList | Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum computing offers polynomial or even exponential speedup that turns out to be a promising approach. However, existing quantum methods is fundamentally limited by either poor scalability or insufficient accuracy. In this paper, we propose SAPO, which formally articulates the quantum circuit that seamlessly integrates financial theory and historical data characteristics with quantum algebra. The circuit design is extended from the HHL algorithm incorporating mean-variance theory, which promotes scalability by equivalent transformation. Then, we present a min-max eigenvalue model that leverages historical financial information to refine parameter settings with high accuracy. Experiments conducted on market data demonstrate that SAPO can effectively reduce the complexity by \mathbf{3 6. 9 4 \%} compared to basic HHL [1], [2] and improve the accuracy by 1.46 \times compared to hybrid HHL [3]. |
| Author | Zhu, Tianze Chen, Yuhang Xi, Meng Lu, Liqiang Yin, Jianwei Zhang, Jinshan Chen, Jiajun Sun, Xiaoming Chen, Hengrui |
| Author_xml | – sequence: 1 givenname: Tianze surname: Zhu fullname: Zhu, Tianze email: tianzezhu@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 2 givenname: Liqiang surname: Lu fullname: Lu, Liqiang email: liqianglu@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 3 givenname: Jiajun surname: Chen fullname: Chen, Jiajun email: cuiox@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 4 givenname: Yuhang surname: Chen fullname: Chen, Yuhang email: chenyuhang@pku.edu.cn organization: Peking University,Beijing,China – sequence: 5 givenname: Hengrui surname: Chen fullname: Chen, Hengrui email: hengruichen@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 6 givenname: Meng surname: Xi fullname: Xi, Meng email: ximeng@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 7 givenname: Jinshan surname: Zhang fullname: Zhang, Jinshan email: zhangjinshan@zju.edu.cn organization: Zhejiang University,Hangzhou,China – sequence: 8 givenname: Xiaoming surname: Sun fullname: Sun, Xiaoming email: sunxiaoming@ict.ac.cn organization: Chinese Academy of Sciences,Beijing,China – sequence: 9 givenname: Jianwei surname: Yin fullname: Yin, Jianwei email: zjuyjw@cs.zju.edu.cn organization: Zhejiang University,Hangzhou,China |
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| Snippet | Portfolio optimization is one of the most important financial problem, suffering from huge computational pressure due to arithmetic complexity. Quantum... |
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| SubjectTerms | Accuracy Complexity theory Eigenvalues and eigenfunctions Optimization Polynomials Portfolios Quantum circuit Quantum mechanics Scalability Transforms |
| Title | SAPO: Improving the Scalability and Accuracy of Quantum Linear Solver for Portfolio Optimization |
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