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IEEE International Conference on Quantum Computing and Engineering (QCE)

An Innovative Hunting-based Quantum-inspired Jaguar Algorithm for Combinatorial Optimization

Yao–Hsin Chou, Jyun–Yi Shen, Yu-Chi Jiang, Shu- Yu Kuo, Cheng-Yen Hua

Published Sep 15, 2024• 0 citations
Publisher

Abstract

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.

DOI
10.1109/qce60285.2024.10401

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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 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) 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.
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.
This paper explores the application of quantum-inspired concepts to the field of combinatorial optimization. Traditional search algorithms often struggle with the complexity of problems involving numerous possible solutions. We propose a novel approach that leverages the principles of quantum superposition and interference, albeit within a classical computing framework, to enhance the efficiency of search processes. The core of our method involves a hybrid algorithm that combines established classical search techniques with probabilistic sampling strategies informed by quantum mechanics. We aim to demonstrate that by mimicking these quantum phenomena, we can significantly improve the speed and effectiveness of solving complex combinatorial optimization problems. The algorithm's performance is evaluated through theoretical analysis and conceptual design, focusing on the potential for achieving performance gains without requiring actual quantum hardware. The key contribution lies in the strategic application of probabilistic sampling to explore solution spaces in a manner analogous to quantum superposition, followed by interference-like mechanisms to guide the search towards promising regions. This approach represents a new direction in optimization, bridging the gap between classical and quantum computing paradigms.
This paper introduces a novel stochastic search algorithm, Quantum-Inspired Stochastic Search (QISS), designed to tackle complex combinatorial optimization problems. QISS draws inspiration from the principles of quantum superposition and entanglement, adapting them into a classical probabilistic framework. Unlike traditional stochastic search methods that rely solely on classical probability distributions, QISS represents potential solutions as a superposition of states, exhibiting entanglement-like correlations to guide exploration. This approach aims to dramatically accelerate the search process by effectively exploring a wider range of the solution space. The core mechanism involves iteratively updating the probabilities associated with each state in the superposition, influenced by both random fluctuations and the observed correlations between states. The algorithm is formulated as a Markov chain Monte Carlo method, where the state transition probabilities are governed by a probability distribution derived from the problem's cost function. The effectiveness of QISS is evaluated through theoretical analysis and, importantly, through a conceptual framework that highlights its potential advantages over existing stochastic optimization techniques. The ultimate goal is to demonstrate that incorporating quantum-inspired concepts, even within a purely classical setting, can lead to significant improvements in the efficiency and robustness of combinatorial optimization algorithms.