Quantum-Inspired Optimization for Public-Finance Efficiency

Samuel Mugel, Enrique Lizaso, Román Orús

Lu, Ying-Chang, Chao‐Ming Fu, Lien-Po Yu +2 more

A quantum-inspired optimization approach is proposed to study the portfolio optimization aimed at selecting an optimal mix of assets based on the risk-return trade-off to achieve the desired goal in investment. By integrating conventional approaches with quantum-inspired methods for penalty coefficient estimation, this approach enables faster and accurate solutions to portfolio optimization which is validated through experiments using a real-world dataset of quarterly financial data spanning over ten-year period. In addition, the proposed preprocessing method of two-stage search further enhances the effectiveness of our approach, showing the ability to improve computational efficiency while maintaining solution accuracy through appropriate setting of parameters. This research contributes to the growing body of literature on quantum-inspired techniques in finance, demonstrating its potential as a useful tool for asset allocation and portfolio management.

Manoj Kumar Vandanapu, Asmath Shaik, Satish Kumar Nagamalla +1 more

This article explores the transformative role of quantum-inspired AI in optimizing financial practices, particularly within high-frequency trading (HFT) in the financial sector. As HFT operates in an environment of rapid transactions and significant market volatility, the need for advanced optimization techniques becomes paramount. Quantum-inspired algorithms leverage principles from quantum mechanics, such as superposition and tunneling, to enhance various aspects of trading strategies. These algorithms enable rapid optimization of asset allocation, real-time trade execution, and proactive fraud detection, effectively addressing the challenges posed by traditional financial models. By facilitating simultaneous evaluation of multiple strategies and enabling real-time analysis of complex trading patterns, quantum-inspired AI significantly improves decision-making speed and accuracy. The financial implications of this advancement are profound, leading to higher profitability, improved market integrity, and enhanced trust among market participants. Ultimately, integrating quantum-inspired AI in finance represents a crucial step towards harnessing cutting-edge technology to reshape trading dynamics, paving the way for innovative strategies that can adapt to the evolving landscape of financial markets. This study underscores the potential of quantum-inspired AI to redefine operational efficiency in finance, ensuring competitiveness in an increasingly complex trading environment.

Tatsuru Kikuchi

Jincheng Zhang

Large-scale combinatorial optimization problems, arising in diverse fields such as logistics, finance, and operations research, pose a significant challenge to classical optimization algorithms. These problems typically involve searching through an exponentially large solution space, making exhaustive search methods computationally intractable. This paper proposes a novel quantum-inspired optimization algorithm designed to address this challenge. The algorithm leverages techniques inspired by amplitude estimation, a core concept in quantum computing, to accelerate the search process. Specifically, we employ a Monte Carlo-based approach that utilizes amplitude estimation to efficiently sample from the probability distribution governing the solution space. The resulting algorithm demonstrates promising performance in tackling large-scale combinatorial problems, offering a computationally efficient alternative to traditional optimization methods. We demonstrate the algorithm's effectiveness through theoretical analysis and provide insights into its advantages and limitations. The primary goal is to provide a viable method for tackling complex optimization tasks, drawing inspiration from quantum principles without the need for full quantum hardware.

한양대학교 박사과정, MyeongSu Choi, Chanyoung Eom +1 more