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 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.
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 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.
Advancements in quantum machine learning offer unprecedented potential to revolutionize financial portfolio optimization, maximizing returns while managing risks efficiently. This study focuses on advancing quantum machine learning algorithms for optimal financial portfolio management, presenting a novel approach implemented in Python that outperforms existing methods. The algorithm's capability to generate such substantial returns over time positions it as a groundbreaking tool for portfolio optimization in the dynamic landscape of financial markets. In the pursuit of enhancing quantum machine learning algorithms, this research focuses on the development and optimization of the QSVM algorithm. Leveraging Python for implementation, the study considers critical factors such as quantum circuit optimization, noise mitigation, and the integration of classical and quantum components to achieve superior results. The achieved portfolio performance over time not only underscores the algorithm's efficacy but also signifies a quantum advantage in financial decision-making. The implementation in Python ensures accessibility and applicability, facilitating the integration of this advanced quantum algorithm into existing financial frameworks. This research contributes to the evolving field of quantum finance, showcasing the potential of quantum machine learning in optimizing financial portfolios. The findings not only validate the superior performance of the proposed QSVM but also highlight the broader implications for the future of financial decision support systems, where quantum algorithms could play a transformative role in enhancing portfolio management strategies. The proposed Quantum Support Vector Machine (QSVM) demonstrates unparalleled success, with a remarkable Portfolio performance over time of 89.65%. This result significantly surpasses existing quantum algorithms, including Quantum Principal Component Analysis (QPCA), Quantum Boltzmann Machines (QBM), and Quantum K-Means Clustering (QKC), by an impressive margin of 25.15%.
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.
Portfolio optimization under conditions of risk and market uncertainty remains a foundational challenge in financial economics and investment management. Classical optimization frameworks, including mean–variance models and stochastic programming approaches, often struggle with high-dimensional asset universes, nonlinear correlations, and dynamic market regimes. This paper examines quantum computing approaches to portfolio optimization under risk and market uncertainty, focusing on hybrid quantum–classical algorithms designed to enhance combinatorial optimization and probabilistic modeling. By integrating quantum annealing, variational quantum circuits, and quantum-enhanced sampling techniques with classical financial analytics, the study evaluates their potential to improve computational efficiency and scenario sensitivity. The analysis considers both theoretical advantages and practical limitations within the Noisy Intermediate-Scale Quantum (NISQ) era. The findings suggest that quantum-assisted optimization may complement traditional portfolio models by expanding feasible solution spaces and improving robustness under uncertainty, thereby contributing to more adaptive and resilient investment strategies.