Presentation
SAPO: Improving the Scalability and Accuracy of Quantum Linear Solver for Portfolio Optimization
DescriptionPortfolio optimization problem stands as one of the most important financial service, which suffers from huge computational pressure due to arithmetic complexity. While, quantum computing has developed rapidly over the last few decades, offering 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 stock market data demonstrate that SAPO can effectively reduce the complexity by 36.94% compared to basic HHL and improve the accuracy by 1.46X compared to hybrid HHL.
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 stock market data demonstrate that SAPO can effectively reduce the complexity by 36.94% compared to basic HHL and improve the accuracy by 1.46X compared to hybrid HHL.
Event Type
Research Manuscript
TimeTuesday, June 241:30pm - 1:45pm PDT
Location3003, Level 3