As modern data environments continue to scale in volume and dimensionality, classical metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) struggle to balance convergence efficiency with solution quality. The challenge is further amplified in big data optimization, where stagnation in local minima and prohibitive computation time impede real-time applicability. Existing methods fail to simultaneously address the need for global search intelligence and computational scalability, especially under high-dimensional constraints and distributed workloads. To bridge this gap, we propose QIESO—a Quantum-Inspired Evolutionary Swarm Optimizer that integrates quantum amplitude encoding, probabilistic gate rotation, swarm coordination, and tunnelingbased mutation to escape local traps and accelerate convergence. Unlike traditional optimizers, QIESO leverages quantum principles simulated classically to maintain coherence across the search space while reducing evaluation overhead. Empirical validation across datasets up to 100 million records shows QIESO achieves a 51.7% reduction in runtime over GA and 42.7% faster convergence than PSO, with a relative error margin consistently under 2.1%. The method exhibits sublinear scalability in time complexity due to dimensionality-agnostic encoding. This work significantly advances scalable optimization for high-throughput environments, making it ideal for applications in autonomous systems, real-time analytics, and next-gen intelligent infrastructures.
As modern data environments continue to scale in volume and dimensionality, classical metaheuristics like Genetic Algorithms (GA) and Particle Swarm Optimization (PSO) struggle to balance convergence efficiency with solution quality. The challenge is further amplified in big data optimization, where stagnation in local minima and prohibitive computation time impede real-time applicability. Existing methods fail to simultaneously address the need for global search intelligence and computational scalability, especially under high-dimensional constraints and distributed workloads. To bridge this gap, we propose QIESO—a Quantum-Inspired Evolutionary Swarm Optimizer that integrates quantum amplitude encoding, probabilistic gate rotation, swarm coordination, and tunnelingbased mutation to escape local traps and accelerate convergence. Unlike traditional optimizers, QIESO leverages quantum principles simulated classically to maintain coherence across the search space while reducing evaluation overhead. Empirical validation across datasets up to 100 million records shows QIESO achieves a 51.7% reduction in runtime over GA and 42.7% faster convergence than PSO, with a relative error margin consistently under 2.1%. The method exhibits sublinear scalability in time complexity due to dimensionality-agnostic encoding. This work significantly advances scalable optimization for high-throughput environments, making it ideal for applications in autonomous systems, real-time analytics, and next-gen intelligent infrastructures.
High-dimensional medical datasets pose a persistent challenge for artificial intelligence because traditional classification algorithms often incur escalating computational costs and reduced predictive accuracy. As healthcare systems generate increasingly complex clinical records, imaging outputs, and genomic profiles, scalable analytic methods that balance precision and efficiency are critical. This study proposes a Quantum-Inspired Optimization (QIO) framework for efficient and accurate classification of high-dimensional healthcare data. Leveraging the exploratory power of variational quantum algorithms, specifically techniques analogous to the Quantum Approximate Optimization Algorithm, the framework integrates quantum-style search strategies with classical computation to achieve global optimization and numerical stability. Publicly available medical datasets with hundreds of features were used to evaluate the approach. Classification models were trained and tested across varying feature dimensionalities, and performance was assessed using accuracy, runtime, and scalability metrics. Empirical results demonstrate that QIO achieves up to 95.4% classification accuracy and reduces computational time by 40% compared with state-of-the-art classical baselines. The method demonstrates stable convergence and clear decision boundaries even as feature dimensionality grows, highlighting its resilience to the curse of dimensionality. These results indicate that QIO can enable fast and reliable healthcare analytics in data-rich clinical environments. Future research may examine domain-specific adaptations, real-time deployment, and integration with emerging quantum hardware to enhance the impact of quantum-inspired artificial intelligence further.
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.
Large-scale optimization (LSO) problems—those with hundreds to thousands of decision variables—are prevalent in engineering design, logistics, telecommunications, and machine learning hyperparameter search. Classical evolutionary algorithms often struggle in this regime due to premature convergence and prohibitive search costs. This manuscript presents a Quantum-Inspired Genetic Algorithm (QIGA) that transfers three ideas from quantum computation into a purely classical algorithm: (i) Q-bit angle encoding that maintains a compact, uncertainty-aware distribution over decision values, (ii) rotation-gate adaptation that updates search probabilities toward promising regions while preserving exploration, and (iii) quantum observation that draws diverse candidate solutions from the encoded amplitudes at each generation. We further couple the core QIGA with (a) an island-model decomposition for ultra-high dimensionality, (b) surrogate-assisted local refinement for exploitation at low cost, and (c) a feasibility-aware penalty for constraints. We evaluate the approach on five canonical continuous benchmarks in 1,000 dimensions (Sphere, Rosenbrock, Rastrigin, Ackley, Griewank) under a fixed computational budget. Across 30 independent runs per function, QIGA achieves lower final objective values and higher success rates than classical GA, PSO, and DE baselines. A non-parametric Wilcoxon signed-rank test indicates statistically significant improvements for QIGA (adjusted p < 0.01) on all functions. We analyze the algorithm’s scaling behavior, the role of angle-space step control, and the contribution of island-level diversity. The results suggest that quantum-inspired probability evolution provides a robust inductive bias for LSO by decoupling representation capacity from point estimates and by enabling distribution-aware search moves. We conclude with practical guidelines for tuning and discuss extensions to constrained and mixed-variable problems.
Adaptive quantum machine learning (QML) represents an exciting frontier at the intersection of quantum computing and classical bio-inspired optimization. The constraints of noisy intermediate-scale quantum (NISQ) devices—limited qubit counts, significant decoherence, and measurement uncertainties—pose formidable challenges to training parameterized quantum circuits (PQCs) using traditional gradient-based methods. To overcome these hurdles, we propose a novel Adaptive PSO–QML framework that integrates a particle swarm optimization (PSO)–inspired algorithm with dynamic hyperparameter adjustment, enabling real-time tuning of PQC parameters based on performance feedback. Unlike static optimizers, our approach continuously adapts its inertia weight and cognitive/social coefficients to balance exploration and exploitation, responding to stagnation or rapid improvement in measured fitness. We evaluate this hybrid algorithm on two benchmark classification tasks: the classical Iris dataset encoded into a 2-qubit circuit and a quantum-encoded subset of the MNIST dataset on 4 qubits. Our experiments, conducted on the Qiskit Aer simulator under realistic noise models (depolarizing, amplitude damping, and mixed), demonstrate that Adaptive PSO–QML achieves an average accuracy improvement of 12% over fixed-parameter PSO and 15% over Adam-based gradient descent, with p-values below 0.01 across paired t-tests. Statistical analysis summarized in Table 1 confirms the significance of these gains. Detailed simulation research further reveals that the adaptive mechanism not only accelerates convergence by approximately 30%—reducing total circuit evaluations—but also exhibits robust performance across varying circuit depths (1–3 entangling layers) and noise intensities. Our findings suggest that bio-inspired adaptation is a powerful tool for mitigating barren plateaus and noise effects in NISQ-era QML, paving the way for scalable quantum advantage in near-term hardware.
In this paper a new optimization algorithm named Adaptive Hybrid Quantum-Inspired Optimization Algorithm (AHQSOA) is presented for solving the complex optimization problems effectively. Stochastic traditional algorithms typically fail to come to be stuck in local optimum solutions and they need quite few iterations for converging. Our method integrates two quantum-inspired methodology: Quantum Particle Swarm Optimization (QPSO) for searching a large set solutions based on properties of quantum mechanics - superposition, point are located as randomly as possible, and Quantum Evolutionary Algorithms (QEA) for refining potential solutions by mutation and crossover operation. An adaptive tuning component based on the reinforcement learning dynamically adjusts the algorithm’s parameters, thus balancing the exploration in the wide interval and exploitation in the precise interval. Assessed on the commonly used benchmark functions like Sphere, Rosenbrock, Rastrigin, AHQSOA exhibits faster convergence (only 400 iterations in comparison to 1200 in standard methods), global search superiority (94% whereas 75–87% the traditional methods), and low computational simplicity. These results show all of our algorithm are nicer and faster and more efficient and robust makes for it a good candidate as for high dimensional and complex optimization problems.
Modern businesses are facing more and more challenges in terms of Big Data warehousing and real-time data analytics due to the sheer volume of growing data. Traditional optimization techniques are increasingly ineffective when working with high-dimensional datasets, thus creating delays in resource allocation and query processing. Since there is a growing demand for faster solutions for data analytics, it is necessary to find new methods for solving optimization issues. In this respect, new emerging quantum-inspired computing techniques allow solving complex optimization problems by employing the principles of quantum mechanics—superposition and probability exploration. To enhance the performance of systems used for predictive analytics and data warehousing, a quantum-inspired optimization framework is proposed. This approach involves incorporating quantum-inspired techniques such as simulations of quantum annealing processes into frameworks used for distributed processing, thus resulting in the improvement of the areas of data indexing, query execution paths and load balancing of computers. The data processing machines are improved due to the advancements in feature selection and attainment of results in a faster way. Evaluation of the method on the big datasets showed that the query performance, and the accuracy of prediction is higher than that demonstrated by conventional techniques, and the architecture is efficient and can be scaled for processing the workloads for real-time analytics.