Obtaining efficient optimisation algorithms has become the focus of much research interest since current developing trends in machine learning, traffic management, and other cutting-edge applications require complex optimised models containing a huge number of parameters. At present, computers based on the classical Turing-machine are inefficient when intent to solve optimisation tasks in complex and wicked problems. As a solution, quantum computers that should satisfy the Deutsch-Church-Turing principle have been proposed but this technology is still at an early stage. quantum-inspired algorithms (QIA) have emerged trying to fill-up an existing gap between the theoretical advances in quantum computation and real quantum computers. QIA use classical computers to simulate some physical phenomena such as superposition and entanglement to perform quantum computations. This paper proposes the quantum-inspired Acromyrmex evolutionary algorithm (QIAEA) as a highly efficient global optimisation method for complex systems. We present comparative statistical analyses that demonstrate how this nature-inspired proposal outperforms existing outstanding quantum-inspired evolutionary algorithms when testing benchmark functions.
Obtaining efficient optimisation algorithms has become the focus of much research interest since current developing trends in machine learning, traffic management, and other cutting-edge applications require complex optimised models containing a huge number of parameters. At present, computers based on the classical Turing-machine are inefficient when intent to solve optimisation tasks in complex and wicked problems. As a solution, quantum computers that should satisfy the Deutsch-Church-Turing principle have been proposed but this technology is still at an early stage. quantum-inspired algorithms (QIA) have emerged trying to fill-up an existing gap between the theoretical advances in quantum computation and real quantum computers. QIA use classical computers to simulate some physical phenomena such as superposition and entanglement to perform quantum computations. This paper proposes the quantum-inspired Acromyrmex evolutionary algorithm (QIAEA) as a highly efficient global optimisation method for complex systems. We present comparative statistical analyses that demonstrate how this nature-inspired proposal outperforms existing outstanding quantum-inspired evolutionary algorithms when testing benchmark functions.
Quantum-Inspired Evolutionary Algorithms (QIEAs) represent a novel fusion of quantum computing principles and classical evolutionary strategies, offering a powerful approach to solving complex optimization problems. This chapter provides a comprehensive overview, detailing foundational concepts, implementation techniques, and potential applications of QIEAs. By leveraging quantum phenomena such as superposition and entanglement, QIEAs enhance the exploitation and exploration capabilities of traditional evolutionary algorithms (EAs), thus enabling an efficient search of vast solution spaces. The study examines key components of QIEA, including quantum encoding, rotation gates, and entanglement operations, and compares its performance with classical EAs. By analyzing real-life examples of combinatorial optimization problems (COPs) such as the minimum spanning tree (MST), traveling salesman problem (TSP), and graph coloring, the chapter demonstrates QIEA’s superior efficiency and solution quality. Despite implementation challenges, QIEA shows promise in various domains, including machine learning, finance, healthcare, telecommunications, and e-commerce. Future research directions are outlined, emphasizing the need for simplified quantum-inspired operators, hybrid quantum-classical systems, and interdisciplinary collaboration. This chapter highlights the transformative potential of QIEA, setting the stage for further advancements and applications in optimization and computational efficiency.
Quantum-inspired algorithms (QIAs) are computationally efficient algorithms that generate a quantum model in classical hardware as an alternative to the use of quantum models. In this research paper, the researchers carried out an experimental appraisal of four quantum-inspired algorithms in quantum-inspired Evolutionary Algorithm (QEA), quantum-behaved Particle Swarm Optimization (QPSO), quantum-inspired Genetic Algorithm (QIGA) and quantum Simulated Annealing (QSA) on benchmark optimization functions. The introduction of a simulation-based methodology was done in Python to test the convergence speed, global search capability, accuracy and robustness. According to the experimental findings, QPSO always performs well compared with other methods in the achievement of faster convergence and enhanced global optimality, but QEA is highly successful in cases of discrete combinatorial problems. The paper concludes that quantum-inspired models are effective in modeling the key quantum concepts like probabilistic superposition and tunneling to attain high quality optimization results with conventional computing platforms. Such results demonstrate the opportunities of QIAs as scalable pre-quantum tools of designing, AI, and industrial optimization.
The increasing complexity of artificial intelligence (AI) has created a need for effective optimisation of highdimensional, nonlinear, and computationally expensive problems. Quantum-inspired optimisation provides an alternative approach by employing quantum-inspired representations and search mechanisms within classical computing systems. This review examines major approaches, including Quantum-Inspired Evolutionary Algorithms (QIEA), Quantum-Inspired Genetic Algorithms (QIGA), Quantum-Inspired Particle Swarm Optimisation (QIPSO), quantum annealing-related approaches, and hybrid methods. Their applications in machine learning, feature selection, hyperparameter optimisation, reinforcement learning, healthcare, finance, cybersecurity, robotics, and smart manufacturing are reviewed. Particular emphasis is placed on solution quality, convergence behaviour, computational cost, scalability, robustness, and reproducibility. The review identifies inconsistent benchmarking, parameter sensitivity, computational overhead, and limited reproducibility as major challenges. The findings indicate that quantum-inspired optimisation should not be considered universally superior to classical optimisation, as its effectiveness depends on problem structure and evaluation conditions. Future research should focus on scalable, hybrid, explainable, and reproducible optimisation frameworks for realistic large-scale AI applications.
By violating the extended Church-Turing thesis, the quantum computing (QC) paradigm has emerged as a serious contender to classical computing. We now know that certain classical exponential problems (NP (nondeterministic polynomial time)-complete) can be solved in polynomial time by QC. Shor’s integer factorization and Grover’s method for unstructured search are perhaps the most well-known quantum algorithms. QC has been successfully applied to a variety of domains including optimization problems. Optimization can be solved by using deterministic/numerical as well as stochastic methods. Genetic algorithms (GA) belong to the latter category. Incorporating quantum mechanical properties such as superposition, entanglement, interference into the structure of GA has resulted in a more efficient evolutionary computing method called the quantum-inspired genetic algorithm. This method has been successfully applied to image processing, signal processing, economic load dispatch, coalition formation, task scheduling, etc. This chapter presents an overview of the current trends and developments in the domain of quantum-inspired genetic algorithms with the aim of investigating areas of further research in this sphere.
Quantum-Inspired Algorithms (QIAs) combine principles of quantum computing with classical evolutionary strategies to address complex optimization problems. This research explores the potential of QIAs in improving optimization processes, particularly in combinatorial and multi-objective optimization scenarios. The study focuses on the application of Quantum-Inspired Genetic Algorithms (QIGAs) and Quantum-Inspired Evolutionary Algorithms (QIEAs), assessing their effectiveness in solving classical problems like the Traveling Salesman Problem (TSP) and Minimum Spanning Tree (MST). Through computational simulations, the research compares the time convergence and solution accuracy of QIAs against traditional classical algorithms. The findings demonstrate that QIAs achieve faster convergence rates and higher-quality solutions, with accuracy levels reaching 98-99% of the global optimal solutions, while significantly reducing computational time. These results underline the advantages of QIAs in solving large and complex optimization problems, making them a promising alternative to traditional algorithms. Additionally, QIAs excel in avoiding local minima, a common pitfall of classical methods, due to their ability to explore the solution space more efficiently through quantum principles like superposition and interference. The implications of this study suggest that QIAs can be a valuable tool for tackling real-world optimization challenges, with potential applications in fields such as finance, logistics, telecommunications, and energy management. The research also indicates the necessity for further improvements in quantum-inspired algorithms' scalability and hardware integration, particularly for larger, more intricate optimization problems, to fully realize their potential in practical industrial applications.
Structural analysis in high-performance computing (HPC) faces challenges related to computational complexity, energy efficiency, and solution accuracy. This research explores Quantum-Inspired Algorithms (QIAs) as an innovative approach to enhance computational efficiency and accuracy in large-scale structural simulations. The proposed methodology integrates a Quantum-Inspired Evolutionary Algorithm (QIEA) with a Hybrid Quantum-Inspired Neural Network (HQINN) for improved structural performance prediction. The study evaluates QIAs on three benchmark structural problems: Bridge Load Distribution Analysis – Achieves a computational speed-up of 45% compared to classical solvers while maintaining an error rate of <0.5%. The Quantum-Inspired Variational Monte Carlo (QIVMC) method is applied to solve complex eigenvalue problems, achieving an 8× acceleration in solving large-scale stiffness matrices compared to traditional iterative solvers. Experimental validation on a high-performance computing cluster using 1,024 cores demonstrates a 55% improvement in processing speed and a 37% reduction in energy consumption. Results confirm that Quantum-Inspired Algorithms significantly outperform traditional numerical methods in structural analysis, paving the way for their adoption in next-generation engineering simulations. Future work will focus on hybrid quantum-classical frameworks and their real-world applications in civil, aerospace, and automotive engineering.