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.
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.
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.
This paper proposes a novel evolutionary algorithm inspired by quantum computing, called a quantum-inspired evolutionary algorithm (QEA), which is based on the concept and principles of quantum computing, such as a quantum bit and superposition of states. Like other evolutionary algorithms, QEA is also characterized by the representation of the individual, evaluation function, and population dynamics. However, instead of binary, numeric, or symbolic representation, QEA uses a Q-bit, defined as the smallest unit of information, for the probabilistic representation and a Q-bit individual as a string of Q-bits. A Q-gate is introduced as a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, experiments were carried out on the knapsack problem, which is a well-known combinatorial optimization problem. The results show that QEA performs well, even with a small population, without premature convergence as compared to the conventional genetic algorithm.
Purpose: The development of engineering decision support systems requires effective optimization techniques to deal with complex optimization problems such as energy management, logistics optimization, and resource management. The most popular classical optimization techniques, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are often less effective in terms of optimization performance and are often likely to suffer from slow optimization speed and premature convergence to local optima when dealing with highdimensional optimization search space. The quantum-inspired optimization (QIO) techniques are an emerging class of optimization techniques that attempt to mimic some principles of quantum computing on classical computers to overcome the limitations of classical optimization techniques. The purpose of this research is to develop a quantum-inspired optimization framework to overcome the limitations of classical optimization techniques in engineering decision support systems. Method: The proposed optimization framework incorporates a probabilistic amplitude representation of solutions, rotation-based evolutionary optimization, and simulated annealing acceptance criteria. The optimization framework was tested on two optimization scenarios: a highdimensional optimization problem inspired by a Rastrigin-like function representing energy management optimization and a multi-objective optimization problem inspired by logistics optimization with the goal of minimizing transportation costs and time. The optimization performance and effectiveness of the proposed optimization framework were compared with classical optimization techniques such as GA and PSO. Findings: The proposed optimization framework outperforms traditional methods such as GA and PSO in terms of speed and quality of results. In the high-dimensional problem, the proposed framework achieved an optimal value of 38.96, which is better compared to the results of PSO, which achieved 69.42, and GA, which achieved 137.22. In the multi-objective problem, the proposed framework achieved Pareto results, which show better performance and effectiveness compared to the traditional methods. Conclusion: The optimization results indicate that the quantum-inspired optimization framework is an effective optimization tool that can be used to overcome the limitations of classical optimization techniques in engineering decision support systems. The proposed optimization framework provides a link between classical optimization techniques and emerging quantum optimization techniques while being able to run on classical computers.
This study explores quantum-inspired optimization algorithms (QIOAs) as practical tools for solving constrained combinatorial problems on classical machines. We analyze tasks that can be formulated in Ising or QUBO forms and benchmark three representative approaches: Simulated Bifurcation (SB), Quantum-Inspired Particle Swarm Optimization (QPSO), and a tensor-network (TN) method. A unified evaluation protocol is introduced that captures solution quality, feasibility, runtime, energy consumption, parameter sensitivity, and scalability. To ensure reliable performance, we develop automated penalty-adjustment and schedule-calibration strategies. Experimental results across diverse benchmarks show that SB and TN together yield the best trade-offs between accuracy and runtime: SB is efficient on dense instances with moderate penalties and low energy use, while TN is advantageous for sparse or modular networks, especially on GPUs. QPSO is competitive in smoother landscapes but more sensitive to hyperparameters. By linking performance to structural properties such as spectral gaps and constraint activity, we provide guidance for selecting suitable algorithms. Overall, QIOAs emerge as effective intermediaries between existing classical heuristics and emerging quantum devices, supported by a reproducible, energy-aware evaluation framework.
This paper explores the application of a quantum-inspired genetic algorithm (QIGA) for solving complex optimization problems. Traditional genetic algorithms (GAs) can struggle with the intricacies of high-dimensional and non-linear problems, often encountering issues with premature convergence and local optima. This research proposes a novel approach where a standard GA is modified to incorporate principles inspired by quantum mechanics, specifically utilizing quantum-inspired concepts such as superposition and interference, to enhance the exploration and exploitation phases. The core claim is that QIGA can effectively tackle complex optimization scenarios. The mechanism involves mapping the original problem into the fitness function of the GA, and subsequently using the QIGA to perform the optimization. The results demonstrate the potential of this hybrid approach to improve solution quality and speed, particularly in problems with challenging characteristics. The research highlights a promising direction for combining classical optimization techniques with quantum-inspired ideas, offering a new tool for tackling computationally intensive optimization tasks. ---