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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Published in:2025 62nd ACM/IEEE Design Automation Conference (DAC) pp. 1 - 7
Main Authors: Zhu, Tianze, Lu, Liqiang, Chen, Jiajun, Chen, Yuhang, Chen, Hengrui, Xi, Meng, Zhang, Jinshan, Sun, Xiaoming, Yin, Jianwei
Format: Conference Proceeding
Language:English
Published: IEEE 22.06.2025
Subjects:
Accuracy
Complexity theory
Eigenvalues and eigenfunctions
Optimization
Polynomials
Portfolios
Quantum circuit
Quantum mechanics
Scalability
Transforms
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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].
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
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  organization: Zhejiang University,Hangzhou,China
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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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