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IEEE International Conference on Quantum Computing and Engineering (QCE)

Qutrit-Based Quantum-Inspired Optimization Model on Real-World Portfolio Optimization

Yao–Hsin Chou, Yun-Ting Lai, Ming-Ho Chang, Yu-Chi Jiang, Shu–Yu Kuo

Published Sep 15, 2024• 3 citations
Publisher

Abstract

Quantum three-level qutrit systems can achieve high-fidelity operations and are favorable for quantum error correction, which is crucial in the current NISQ era. Before the advent of fault-tolerant computers, quantum-inspired optimization (QIO) shows potential as a practical near-term application of quantum computing to address real-world problems. Consequently, we have developed a novel encoding mechanism leveraging the quantum three-level qutrit concept to propose a qutrit-based QIO for the quantum finance model. This approach comprehensively addresses financial applications by encoding additional strategic information, unlike traditional methods that only consider binary buy-or-not-buy decisions. In a volatile eco-nomic environment, when the corresponding properties of qutrits are simulated, multiple strategies can be employed to stabilize investment efficiency, including both long-selling (LS) and short-selling (SS) strategies. The experimental results demonstrate that the proposed qutrit-based model can construct a portfolio with superior performance compared to the two single strategies when using real-world financial data. Furthermore, this study reveals that the fluctuations from LS and SS strategies can be complementary within a hybrid strategy, contributing to a significant reduction of risk. The proposed qutrit-based method uses the advantages of quantum behavior in a large solution space, searches for potential solutions efficiently in real-world applications, and aids investors in making an informed decision.

DOI
10.1109/qce60285.2024.10403

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Quantum three-level qutrit systems can achieve high-fidelity operations and are favorable for quantum error correction, which is crucial in the current NISQ era. Before the advent of fault-tolerant computers, quantum-inspired optimization (QIO) shows potential as a practical near-term application of quantum computing to address real-world problems. Consequently, we have developed a novel encoding mechanism leveraging the quantum three-level qutrit concept to propose a qutrit-based QIO for the quantum finance model. This approach comprehensively addresses financial applications by encoding additional strategic information, unlike traditional methods that only consider binary buy-or-not-buy decisions. In a volatile eco-nomic environment, when the corresponding properties of qutrits are simulated, multiple strategies can be employed to stabilize investment efficiency, including both long-selling (LS) and short-selling (SS) strategies. The experimental results demonstrate that the proposed qutrit-based model can construct a portfolio with superior performance compared to the two single strategies when using real-world financial data. Furthermore, this study reveals that the fluctuations from LS and SS strategies can be complementary within a hybrid strategy, contributing to a significant reduction of risk. The proposed qutrit-based method uses the advantages of quantum behavior in a large solution space, searches for potential solutions efficiently in real-world applications, and aids investors in making an informed decision.
Portfolio optimization is an essential aspect of the development of quantum computing. The quantum-inspired op-timization (QIO) algorithm facilitates an efficient search for the optimal solution by simulating quantum mechanics on a classical computer, thus bridging quantum theory and the actual implementation of quantum computing. This study proposes an entanglement-based QIO to optimize the short-selling portfolio in a group of seven (G7) industrialized nations, which are the world's largest markets and significantly impact global economies. To diversify investment options in response to the ever-changing markets, short-selling is a worthy topic for dis-cussion. The innovative trend ratio can precisely determine the performance of a short-selling portfolio during a stable downward trend. Implementing the short-selling trend ratio model in the significant G7 markets broadens its applicability.
Quantum finance has recently attracted significant attention for its potential to enhance investment strategies.This work introduces an entanglement-enhanced quantum-inspired optimization (QIO) system for dynamic portfolio management across the global Group of Seven (G7) markets. A comprehensive analysis is conducted over both training and testing phases to demonstrate the system’s robustness and practical utility in real-world financial scenarios. Extensive experiments validate its profitability under various sliding window configurations across different markets. The results reveal both shared patterns and distinct differences among G7 countries. In addition, the system’s performance under volatile market conditions, particularly during the COVID-19 pandemic, is examined to highlight its resilience and adaptability. These findings underscore the practical value of QIO-based systems for adaptive portfolio management in complex, interconnected markets and suggest promising directions for future research in cross-market analysis and advanced optimization strategies.
Quantum-inspired optimization (QIO) has gained attention for leveraging both quantum and classical advantages to enhance search efficiency in solving complex optimization problems on classical computers. While most QIO approaches have shown strong potential in addressing single-objective optimization, real-world problems often involve conflicting objectives, making them inherently multi-objective and challenging to solve. Unlike single-objective optimization, which seeks a single optimal solution, multi-objective optimization aims to obtain a set of Pareto-optimal solutions. This study extends QIO to multi-objective optimization (MoQIO) by incorporating quantum-inspired characteristics and provides an in-depth analysis of its performance. It utilizes quantum superposition and Q-gates to enhance convergence efficiency toward the Pareto front, while leveraging entanglement properties to expand solution diversity. Our results demonstrate that the proposed MoQIO effectively generates a diverse set of optimal portfolio solutions.
The relentless quest for optimized financial portfolios has led to the exploration of advanced computational techniques capable of navigating financial markets’ complex and dynamic landscape. This dissertation presents a solution for portfolio optimization by harnessing the synergistic potential of quantum computing and machine learning. We propose a hybrid framework that integrates quantum algorithms’ optimization capabilities with machine learning’s predictive accuracy to address the multifaceted challenges of portfolio management. The study begins with a comprehensive review of traditional and contemporary portfolio optimization methods and an in-depth analysis of quantum computing principles relevant to optimization problems. Next, we examine machine learning models commonly used for forecasting financial time series, which serve as critical inputs for the quantum optimization process. The proposed solution is evaluated through simulations and real-world financial data to demonstrate its efficacy in achieving optimized asset allocations with enhanced risk-adjusted returns. This research contributes to the theoretical advancement of financial optimization techniques and provides practical insights for investors and portfolio managers seeking to leverage emerging technologies for strategic decision-making.
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.
Quantum computing has emerged as a revolutionary computational paradigm capable of solving complex optimization and probabilistic problems that are computationally expensive for classical computers. The financial industry, characterized by large-scale datasets, uncertainty, stochastic behavior, and multidimensional optimization challenges, is increasingly exploring quantum computing to enhance decision-making processes. This paper presents a comprehensive investigation into the applications of quantum computing in financial modeling, emphasizing portfolio optimization, derivative pricing, risk assessment, fraud detection, and algorithmic trading. Unlike conventional computational techniques that suffer from exponential complexity when processing high-dimensional financial data, quantum algorithms leverage superposition, entanglement, and quantum parallelism to improve computational efficiency and solution quality. The study discusses fundamental quantum computing concepts, analyzes existing financial modeling frameworks, and examines how quantum algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), Quantum Monte Carlo (QMC), Grover’s Search Algorithm, and Quantum Machine Learning (QML) contribute to solving real-world financial problems. Furthermore, the paper reviews recent research developments, identifies implementation challenges associated with noisy intermediate-scale quantum (NISQ) devices, and highlights future opportunities for hybrid quantum-classical financial systems. The findings demonstrate that quantum computing has significant potential to transform computational finance by improving prediction accuracy, accelerating optimization tasks, and enabling scalable financial analytics for next-generation intelligent financial ecosystems.