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IEEE International Conference on Nanotechnology (IEEE-NANO)

Quantum-inspired Computing: Entanglement-enhanced Technique for Short Portfolio in Global Markets

Yu-Chi Jiang, Yun-Ting Lai, Po-Chun Chen, Yuyu Chang, Kun-Min Wu, Shu–Yu Kuo, Yao–Hsin Chou, Sy‐Yen Kuo

Published Jul 2, 2023• 6 citations
Publisher

Abstract

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.

DOI
10.1109/nano58406.2023.10231234

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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 computing is a next-generation computing paradigm that offers potential advantages for addressing complex real-world applications, such as portfolio optimization (PO). Quantum-inspired optimization (QIO) algorithms simulate quantum mechanisms to achieve quantum advantages using classical computers. QIO algorithms can serve as a bridge to realizing preliminary quantum advantages by exploiting classical computation abilities. In a detailed analysis of PO, a domain-dependent optimization technique is proposed combined with an effective QIO, called the entanglement local search-assisted (ELSA) technique. ELSA introduces the novel concept of the entangled neighborhood for PO and assists QIO in accurately searching a potential area, thus significantly promoting the quality of the solution. The entanglement relationship can decrease the degree of freedom searched. The proposed ELSA-QIO technique is employed in a trend ratio-based intelligent system to search for a steady uptrend portfolio in the global G20 markets. This study discusses the expanded markets to demonstrate the superior ability of the proposed QIO method in a vast solution space. Experiments demonstrate that the proposed system outperforms state-of-the-art methods. This study investigates the world's largest markets in-depth and proposes a strategy for global financial management that expands the use of quantum computing.
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 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.
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.
Quantum and quantum-inspired computations have shown remarkable potential in addressing complex optimization problems. Quantum annealing (QA), in particular, has demonstrated effectiveness in solving combinatorial optimization tasks and is gaining increasing attention. Meanwhile, quantuminspired computing (QIC) algorithms offer a promising approach to leveraging quantum principles on classical hardware. This study presents the first integrated hybrid framework that combines the strengths of both quantum and classical computing for multi-objective weighted portfolio optimization, a complex real-world application. In the proposed approach, QA employs scalarization techniques to rapidly approximate the optimal front, while QIC enhances solution diversity through an entanglement move mechanism that uncovers a broader set of non-dominated portfolio solutions. Experimental results indicate that the hybrid approach achieves higher-quality solutions and provides a greater variety of investment options for investors, compared to QA alone and classical multi-objective optimization algorithms. These findings highlight the feasibility and scalability of integrating quantum and classical techniques to effectively address real-world investment optimization challenges.
This study explores the integration of quantum computing techniques, specifically the Quantum Approximate Optimization Algorithm (QAOA), with classical portfolio optimization frameworks through the Quadratic Unconstrained Binary Optimization (QUBO) approach. Using monthly turnover data from the National Stock Exchange of India (NSE), this paper demonstrates the construction of a QUBO matrix by encoding expected returns and covariance-based risk profiles. Binary asset inclusion constraints are introduced into portfolios and then optimised using both brute force enumeration and quantum inspired solvers. The analysis reveals those combinations including Index Futures and Index Options exhibit superior risk-adjusted performance (Sharpe Ratio≈0.286\approx 0.286≈0.286). The hybrid approach suggests the promise of quantum inspired models as means to improve financial decision making under uncertainty. The results also give a robust template for scalable asset selection models that operate within the constraints of quantum computing infrastructure.