Quantum-inspired algorithms simulate quantum characteristics on classical computers to accelerate the solution of complex real-world optimization problems. In the era before general-purpose quantum computers become practical, they offer a promising and accessible alternative for tackling such challenges. Among optimization problems, combinatorial types are particularly challenging due to discrete spaces, large scales, and strong variable dependencies. This study proposes a novel quantum-inspired algorithm named the quantum-inspired jaguar algorithm (QJA), specifically designed to address highly discrete and structurally complex combinatorial optimization problems. QJA integrates the adaptive hunting mechanism of the jaguar algorithm with the core concept of quantum-inspired tabu search algorithm, which guides the search by moving toward the best solution while avoiding the worst one. QJA dynamically adjusts its parameters based on historical information to accelerate convergence and efficiently explore the solution neighborhood. In addition, the algorithm incorporates an entangled local search mechanism to further enhance solution quality. QJA is evaluated on real-world portfolio optimization using data from the Toronto Stock Exchange. Results show that it outperforms other quantum-inspired algorithms in both stability and solution quality, demonstrating superior convergence and practical applicability.
Quantum-inspired algorithms simulate quantum characteristics on classical computers to accelerate the solution of complex real-world optimization problems. In the era before general-purpose quantum computers become practical, they offer a promising and accessible alternative for tackling such challenges. Among optimization problems, combinatorial types are particularly challenging due to discrete spaces, large scales, and strong variable dependencies. This study proposes a novel quantum-inspired algorithm named the quantum-inspired jaguar algorithm (QJA), specifically designed to address highly discrete and structurally complex combinatorial optimization problems. QJA integrates the adaptive hunting mechanism of the jaguar algorithm with the core concept of quantum-inspired tabu search algorithm, which guides the search by moving toward the best solution while avoiding the worst one. QJA dynamically adjusts its parameters based on historical information to accelerate convergence and efficiently explore the solution neighborhood. In addition, the algorithm incorporates an entangled local search mechanism to further enhance solution quality. QJA is evaluated on real-world portfolio optimization using data from the Toronto Stock Exchange. Results show that it outperforms other quantum-inspired algorithms in both stability and solution quality, demonstrating superior convergence and practical applicability.
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.
Quantum-inspired optimization is an emerging computational intelligence technique that achieves quantum advan-tages through inspiration from the mechanisms of quantum properties. Parameter control is vital for optimization methods to address complex problems with extensive solution spaces. However, static parameters lack adaptability, leading to issues like premature convergence, becoming trapped in local optima, and failing to uncover the global optimum. Therefore, we develop a novel adaptive mechanism to dynamically adjust parameters during algorithm execution to strike a remarkable balance between exploration and exploitation. This study first proposes the knowledge-navigated quantum-inspired tabu search (KNQTS) algorithm, which acquires insights from the ongoing process to adjust the environment-sensitive parameters adaptively and non-linearly. Differences between inter-iteration solutions can signal the extent of convergence and guide bidirectional adjustments of a parameter, facilitating the handling of com-plex optimization problems. KNQTS is applied to tackle real-world application portfolio construction problems in the financial technology field. Comprehensive experiments are conducted to demonstrate the substantial potential and robustness of KNQTS in rapidly identifying better solutions with higher probabilities in shorter durations compared to the other state-of-the-art methods. Statistical tests substantiate the significant superiority of KNQTS over these comparative algorithms.
This paper explores the potential of quantum-inspired algorithms for tackling large-scale optimization problems. Traditional optimization methods often struggle with the computational complexity of problems involving numerous variables and constraints. Quantum mechanics, particularly concepts like superposition and entanglement, offers a radically different approach to problem-solving. This work investigates how these concepts can be translated into algorithmic designs, aiming to improve the efficiency and speed of optimization processes. We propose a framework for developing algorithms that mimic quantum phenomena, focusing on techniques that enable exploration of the solution space in a more parallel and potentially faster manner. The core of this approach lies in leveraging mathematical structures derived from quantum mechanics to guide search strategies. We outline key algorithmic ideas, emphasizing the potential for improved convergence rates and scalability. The presented concepts represent a novel application of quantum-inspired ideas to the field of optimization, offering a promising avenue for addressing complex real-world problems.
Many metaheuristic algorithms have been proposed to solve combinatorial and numerical optimization problems. Most optimization problems have high dependence, meaning that variables are strongly dependent on one another. If a method were to attempt to optimize each variable independently, its performance would suffer significantly. When traditional optimization techniques are applied to highdependence problems, they experience difficulty in finding the global optimum. To address this problem, this paper proposes a novel metaheuristic algorithm, the entanglement-enhanced quantum-inspired tabu search algorithm (Entanglement-QTS), which is based on the quantum-inspired tabu search (QTS) algorithm and the feature of quantum entanglement. Entanglement-QTS differs from other quantum-inspired evolutionary algorithms in that its Q-bits have entangled states, which can express a high degree of correlation, rendering the variables more intertwined. Entangled Q-bits represent a state-of-the-art idea that can significantly improve the treatment of multimodal and high-dependence problems. Entanglement-QTS can discover optimal solutions, balance diversification and intensification, escape numerous local optimal solutions by using the quantum not gate, reinforce the intensification effect by local search and entanglement local search, and manage strong-dependence problems and accelerate the optimization process by using entangled states. This paper uses nine benchmark functions to test the search ability of the entanglement-QTS algorithm. The results demonstrate that Entanglement-QTS outperforms QTS and other metaheuristic algorithms in both its effectiveness at finding the global optimum and its computational efficiency.
This paper presents a novel quantum-adaptive optimization algorithm designed to address challenges in computationally intensive optimization tasks. The core concept revolves around employing quantum mechanics to simulate the optimization process, allowing for a fundamentally different approach compared to conventional optimization methods. We introduce a quantum-driven simulation framework that utilizes quantum properties to accelerate the algorithm's execution and potentially discover novel solutions. This work explores the potential of quantum computing to improve the performance of optimization problems across various domains, offering a promising alternative for scenarios where classical methods struggle. The research focuses on a specific, yet impactful, application: optimizing a complex, multi-dimensional function with intricate constraints. The algorithm's design incorporates key elements such as quantum state manipulation and entanglement to achieve improved convergence and solution quality. This paper details the theoretical underpinnings, implementation details, and preliminary experimental results demonstrating the algorithm's effectiveness. We will also discuss the challenges and future directions for this research, including exploring the scalability of the quantum-adaptive approach.
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.