This paper presents a Quantum Machine Learning– Driven AI Predictive Model (QML–AI PM) for cluster-level optimization of manufacturing lines. The proposed framework integrates Quantum Features Encoding (QFE), Variation Quantum Circuits (V.Q.C.), and the Quantum Approximate Optimization Algorithm (QAOA) with advanced AI models to address uncertainty, nonlinear behavior, and high-dimensional interactions in industrial environments. A combined dataset of multi-cluster manufacturing operations was used to evaluate the system, and performance was compared with classical models such as SVM, RF, LSTM, and XGBoost. Results show that the proposed model achieves 94.6% prediction accuracy, 93.4% optimization efficiency, and lower computation time, outperforming all baseline techniques. The hybrid architecture significantly improves scheduling, resource allocation, and predictive maintenance at the cluster scale. Overall, the QML–AI PM demonstrates substantial potential for next-generation smart manufacturing and establishes a foundation for future quantum-enabled industrial optimization.
This paper presents a Quantum Machine Learning– Driven AI Predictive Model (QML–AI PM) for cluster-level optimization of manufacturing lines. The proposed framework integrates Quantum Features Encoding (QFE), Variation Quantum Circuits (V.Q.C.), and the Quantum Approximate Optimization Algorithm (QAOA) with advanced AI models to address uncertainty, nonlinear behavior, and high-dimensional interactions in industrial environments. A combined dataset of multi-cluster manufacturing operations was used to evaluate the system, and performance was compared with classical models such as SVM, RF, LSTM, and XGBoost. Results show that the proposed model achieves 94.6% prediction accuracy, 93.4% optimization efficiency, and lower computation time, outperforming all baseline techniques. The hybrid architecture significantly improves scheduling, resource allocation, and predictive maintenance at the cluster scale. Overall, the QML–AI PM demonstrates substantial potential for next-generation smart manufacturing and establishes a foundation for future quantum-enabled industrial optimization.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.
The contemporary manufacturing landscape is more complicated, data-heavy and more automated than ever before, producing enormous volumes of multi-modal sensor data, production logs and maintenance records. Predictive analytics within these environments becomes essential to achieve operational efficiency, reduce downtime, and optimise resource allocation; however, many classical machine learning methods struggle with high-dimensional, nonlinear, and dynamic data, resulting in suboptimal predictions and slow decision-making. To overcome these issues, the proposed research suggests a Quantum-Enhanced AI (QAI) system combining various quantum algorithms: Quantum Neural Networks (Quantum Neural Network) to recognize patterns, Quantum Supporter Vector Machines (Quantum Supporter Vector Machines) to classify, Quantum Approximate Optimization Algorithm (Quantum Approximate Optimization Algorithm) to schedule production and allocate resources, and Variational Quantum Eigensolver (Variational Quantum Eigensolver) to optimize parameters under noisy settings or conditions into a hybrid quantum-class The framework is a quantum computer based framework that encodes classical manufacturing data in the form of quantum states, using amplitude and qubit-efficient embeddings, allowing representation of high-dimensional features with the parallelism and entanglement of quantum computing. Realworld experimental validation of both turbofan engine degradation and semiconductor manufacturing sensor logs was conducted using IBM Qiskit and PennyLane to ensure reproducibility, with noise-aware training and error mitigation policies applied in simulations on NISQ-era hardware. The proposed QAI framework demonstrated higher predictive accuracy (12 to 15) than state-of-the-art hybrid quantum-classical and classical models, with superior accuracy, memorability, and F1 scores, and showed exceptional capabilities in anomaly detection, failure prediction, and optimisation. The results validate that quantum algorithms can be integrated into a predictive analytics pipeline to overcome classical computation bottlenecks, enhance robustness, and provide actionable insights for making real-time manufacturing decisions. Overall, this paper has demonstrated that quantum-assisted AI can be beneficial in intelligent manufacturing environments, laying the groundwork for expanding practical applications that bridge the gap between the promises of quantum computational power and predictive analytics in the industry.
The rapid digitalization of manufacturing systems has led to the widespread adoption of Industrial Internet of Things (IIoT) networks, enabling real-time monitoring, data-driven optimization, and intelligent automation. A critical application within this paradigm is predictive maintenance, which aims to anticipate equipment failures before they occur, thereby reducing downtime, operational costs, and safety risks. However, traditional machine learning approaches for predictive maintenance and anomaly detection often struggle with high-dimensional sensor data, complex nonlinear relationships, and scalability constraints in large industrial environments. This paper explores the emerging role of Quantum Machine Learning (QML) as a novel computational framework for enhancing predictive maintenance and anomaly detection in smart manufacturing systems. By leveraging quantum principles such as superposition and quantum-enhanced feature spaces, QML algorithms offer new possibilities for processing complex industrial data more efficiently and accurately. The study examines hybrid quantum–classical models for fault prediction, early anomaly detection, and pattern recognition in IIoT networks, highlighting their potential advantages over classical methods. Challenges related to hardware limitations, data encoding, and industrial deployment are also discussed, providing a balanced perspective on the feasibility and future impact of quantum-enhanced intelligence in smart manufacturing.
In the rapidly evolving landscape of Industry 4.0, industrial systems have become increasingly complex, interconnected, and data-intensive. Traditional computational methods often fall short in managing the highdimensional, nonlinear challenges presented by such environments. This paper explores a novel framework that integrates Big Data analytics with quantum computing techniques to model and optimize complex industrial systems-including Smart Grids, Supply Chains, and Manufacturing processes. By leveraging algorithms such as Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Quantum Machine Learning (QML), the study demonstrates substantial improvements in processing time, accuracy, and resource efficiency compared to classical methods. Empirical evaluation through a structured experimental design and statistical validation via ANOVA confirms that hybrid quantum-classical approaches outperform traditional models across multiple performance metrics. This integration paves the way for real-time, scalable, and intelligent industrial optimization, offering a transformative leap in operational decision-making, fault prediction, and strategic control.
Predictive maintenance (PdM), a widely used approach for improving reliability, safety, and cost-efficiency in industrial operations, relies on advanced analytics and data-driven insights to detect incipient failures and optimize system performance. While conventional machine learning (ML) techniques are widely applied in PdM, they often struggle with noisy, high-dimensional sensor data. Recent progress in quantum machine learning (QML) offers potentially useful directions for improving fault diagnosis and predictive accuracy. This motivates a systematic literature review that assesses QML applications in PdM, examines technical and practical challenges shaping their development, and identifies opportunities for future research and industrial adoption. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, we screened 238 records and finalized 18 empirical studies published between 2015 and 2025 across five major digital libraries. Our synthesis indicates that QML-for-PdM studies primarily use quantum kernel methods and shallow variational hybrid models, typically employing angle-based encoding, controlled-Z (CZ) entanglement, and circuit depth ≤ 5 . Reported results suggest performance advantages of QML over classical baselines in curated settings, though the supporting evidence remains concentrated in highly controlled, few-qubit experimental environments. However, reported performance degrades under positive–unlabeled noise, over-parameterization, and unmitigated hardware noise. Overall, this review provides an evidence-based assessment of QML-based predictive maintenance by synthesizing reported benefits, practical constraints, and priority directions for future work.
Hybrid quantum–classical simulation offers a novel computational paradigm for addressing the increasing complexity of modern manufacturing systems. As production lines evolve into highly interconnected, data-rich environments, conventional simulation and optimization techniques often struggle to capture nonlinear interactions, dynamic bottlenecks, and stochastic fluctuations in real time. This study proposes a hybrid framework that integrates quantum-inspired state encoding, variational quantum optimization, and classical discrete-event simulation to model and predict bottleneck behavior with enhanced efficiency. By leveraging quantum subroutines—particularly Variational Quantum Eigensolvers (VQEs) and Quantum Approximate Optimization Algorithms (QAOA)—the framework accelerates combinatorial scheduling searches and dynamic resource allocation tasks. Classical processors, in turn, handle high-fidelity system modeling and domain-specific constraints. Preliminary experimental simulations demonstrate that the hybrid approach can identify emergent bottlenecks earlier than purely classical methods and improve process throughput by up to 18% in benchmark scenarios. These results highlight the transformative potential of hybrid quantum–classical systems for next-generation smart factories, enabling more resilient, adaptive, and energy-efficient manufacturing operations.