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 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.
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-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 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) is a promising technique that simulates quantum behaviors on classical computers to leverage both classical and quantum advantages in tackling complex optimization problems. This study first proposes the quantum-inspired jaguar algorithm (QJA) to solve combinatorial optimization problems (COP). The quantum property helps address COP efficiently when the element increases in the search space. Besides, QJA extracts the crucial concept of the hunting mechanism from the jaguar algorithm (JA), which effectively addresses numerical optimization in classical computing. The featured hunting mechanism enables QJA to automatically assess the algorithm's situation and adaptively update the critical parameters in QIO, thereby performing an efficient search. QJA tackles a complex financial application: portfolio optimization, which requires an effective solution quickly. In the experimental result, this study utilizes real-world financial data to demonstrate the effectiveness of QJA and to compare it with other QIOs, facilitating advancements in quantum finance. The convergence results indicate that QJA can identify higher quality portfolios in the early stages and continue to enhance them throughout the search process. In summary, this study transfers the knowledge and insights gained from the hunting mechanism in classical computing to QIO, integrating both classical and quantum computing techniques to address COP effectively.
The increasing complexity of financial markets has heightened the demand for advanced computational methods capable of optimizing portfolios under multi-dimensional constraints involving risk, return, diversification, and liquidity. Traditional optimization frameworks, such as mean-variance analysis and heuristic meta-optimization, are limited by their scalability and sensitivity to non-convex, high-dimensional data structures. This study explores the application of quantum algorithms to portfolio optimization, demonstrating how Quantum Approximate Optimization Algorithm (QAOA), Quantum Annealing, and Variational Quantum Eigensolver (VQE) can address computational bottlenecks inherent in classical approaches. Quantum computation offers the potential to encode complex financial systems as Hamiltonian functions, allowing parallel exploration of vast solution spaces through superposition and entanglement. This enables faster convergence toward optimal portfolio allocations while simultaneously accounting for multi-factor correlations and dynamic market conditions. The research further investigates the role of hybrid quantum–classical optimization pipelines, in which quantum solvers perform core optimization tasks while classical systems manage data preprocessing and constraint validation. Empirical modeling and simulation indicate that quantum-based approaches can outperform conventional methods in risk-adjusted return optimization, efficient frontier computation, and real-time portfolio rebalancing. By bridging the gap between quantum computing and financial engineering, this study contributes to the development of next-generation financial analytics systems, aligning with the broader goal of achieving more adaptive, transparent, and data-resilient financial decision-making.
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.