Predictive maintenance (PdM) functions as a proven industrial 4.0 element which enables organizations to detect issues ahead of time while minimizing system downtimes and improving their maintenance procedures. The predictive maintenance success of Random Forest (RF), Gradient Boosting Machines (GBM) and Deep Neural Networks (DNNs) remains limited by their high computational costs and feature selection inefficiencies and difficulties in scaling for real-time industrial applications. New Quantum Machine Learning (QML) advancements manage to improve predictions through quantum superposition methods alongside kernel transformations and entanglement mechanics for better efficiency. The research introduces Q-PdM methodology that combines QSVM with VQC and Q-DNN for device failure detection in industrial systems through predictive maintenance. The research introduces Q-CBAM as a metric that enables financial feasibility evaluation of quantum-enhanced PdM models. The methodology demonstrates superior performance when implemented with AI4I 2020 Predictive Maintenance Dataset because it surpasses traditional prediction methods in all operational aspects including accuracy rates, processing speed and operational expense reduction. This research validates the Q-DNN model because it delivers 96.2% accuracy alongside 93.5% cost-benefit efficiency however its quantum models minimize computation demands and speed up inference time to suit industrial IoT real-time environments. The presented study enables a key connection between theoretical quantum machine learning research and industrial predictive maintenance practice leading to future quantum-driven monitoring systems for industries.
Predictive maintenance (PdM) functions as a proven industrial 4.0 element which enables organizations to detect issues ahead of time while minimizing system downtimes and improving their maintenance procedures. The predictive maintenance success of Random Forest (RF), Gradient Boosting Machines (GBM) and Deep Neural Networks (DNNs) remains limited by their high computational costs and feature selection inefficiencies and difficulties in scaling for real-time industrial applications. New Quantum Machine Learning (QML) advancements manage to improve predictions through quantum superposition methods alongside kernel transformations and entanglement mechanics for better efficiency. The research introduces Q-PdM methodology that combines QSVM with VQC and Q-DNN for device failure detection in industrial systems through predictive maintenance. The research introduces Q-CBAM as a metric that enables financial feasibility evaluation of quantum-enhanced PdM models. The methodology demonstrates superior performance when implemented with AI4I 2020 Predictive Maintenance Dataset because it surpasses traditional prediction methods in all operational aspects including accuracy rates, processing speed and operational expense reduction. This research validates the Q-DNN model because it delivers 96.2% accuracy alongside 93.5% cost-benefit efficiency however its quantum models minimize computation demands and speed up inference time to suit industrial IoT real-time environments. The presented study enables a key connection between theoretical quantum machine learning research and industrial predictive maintenance practice leading to future quantum-driven monitoring systems for industries.
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.
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.
The economic value of predictive maintenance (PdM) for industrial IoT machinery is undeniable, as unplanned equipment downtime is estimated to cost industries USD 50 billion a year worldwide. Deep learning techniques have achieved good fault classification results on benchmark datasets, but they are not robust enough to cope with noise in industrial environments, are computationally intensive for use at the edge, and are unable to make good use of the capabilities of quantum computing. In this paper, a new hybrid network, called QEML-Net (Quantum-Enhanced Machine Learning Network), is proposed to combine the ResNet-50 deep residual network with Convolutional Block Attention Modules (CBAM), variational quantum feature enhancement, and a compound hybrid loss function, for efficient fault diagnosis. The framework features a 6-layer, 4-qubit Parameterized Quantum Circuit (PQC) with angle encoding and linear CNOT entanglement that is implemented using PennyLane. Experiments were performed on six harmonized datasets from public PdM which have 57164 samples across eight unified fault categories. The framework was validated with the help of Bayesian hyperparameter optimization, 5-fold stratified cross-validation, ablation studies, and statistical testing. By optimizing the deployment on the edges, QEML-Net got 97.3% accuracy, 96.9% macro-F1 score, AUC of 0.988, and real-time inference performance with 19ms latency and 52FPS throughput on the benchmark dataset. The statistical analysis revealed significant improvement when compared to other methods (p < 0.001). Even when evaluated cross-dataset without fine-tuning, good generalization was achieved with an accuracy of 92.8–94.6%. The results reveal the advantages of combining the quantum variational circuits with deep learning to enhance the classification accuracy, interpretability, and deployment efficiency for the fault diagnosis of industrial IoT.
The integration of quantum computing and neural networks has emerged as a transformative approach for addressing complex industrial challenges, particularly in predictive maintenance.This paper is the result of insights gained during the development of a Proof of Concept (PoC) in collaboration with Embraer, a leading aerospace company in Brazil, and SENAI UpLab, an innovation and technology hub of SENAI, one of Brazil’s foremost institutions for industrial education and applied research. Traditional predictive maintenance methods often struggle with the high-dimensional data and complex failure patterns inherent in aerospace systems. Leveraging the principles of quantum mechanics, QNNs and QSVCs offer enhanced computational power and the ability to process vast datasets exponentially faster than classical machine learning approaches. We propose a novel hybrid framework that combines QNNs and QSVCs, utilizing quantum superposition, entanglement, and kernel-based quantum algorithms to model the degradation patterns of robotic arm components. This enables early detection of potential failures and optimizes maintenance schedules with improved accuracy. The framework is validated using real-world data from aerospace robotic systems provided by EMBRAER, demonstrating superior performance in terms of accuracy, efficiency, and robustness compared to classical machine learning methods. Our results, supported by the collaboration with SENAI UpLab, highlight the potential of quantum-enhanced techniques, including QNNs and QSVCs, to revolutionize predictive maintenance in the aerospace industry, reducing downtime, minimizing costs, and enhancing operational safety. This study contributes to the growing body of research at the intersection of quantum computing and industrial applications, paving the way for future advancements in intelligent maintenance systems.
Detecting machine failures promptly is of utmost importance in industry for maintaining efficiency and minimizing downtime. This paper introduces a failure detection algorithm based on quantum computing and a statistical change-point detection approach. Our method leverages the potential of projected quantum feature maps to enhance the precision of anomaly detection in machine monitoring systems. We empirically validate our approach on benchmark multi-dimensional time series datasets as well as on a real-world dataset comprising IoT sensor readings from operational machines, ensuring the practical relevance of our study. The algorithm was executed on IBM's 133-qubit Heron quantum processor, demonstrating the feasibility of integrating quantum computing into industrial maintenance procedures. The presented results underscore the effectiveness of our quantum-based failure detection system, showcasing its capability to accurately identify anomalies in noisy time series data. This work not only highlights the potential of quantum computing in industrial diagnostics but also paves the way for more sophisticated quantum algorithms in the realm of predictive maintenance.
The integration of quantum computing and neural networks has emerged as a transformative approach for addressing complex industrial challenges, particularly in predictive maintenance.This paper is the result of insights gained during the development of a Proof of Concept (PoC) in collaboration with Embraer, a leading aerospace company in Brazil, and SENAI UpLab, an innovation and technology hub of SENAI, one of Brazil’s foremost institutions for industrial education and applied research. Traditional predictive maintenance methods often struggle with the high-dimensional data and complex failure patterns inherent in aerospace systems. Leveraging the principles of quantum mechanics, QNNs and QSVCs offer enhanced computational power and the ability to process vast datasets exponentially faster than classical machine learning approaches. We propose a novel hybrid framework that combines QNNs and QSVCs, utilizing quantum superposition, entanglement, and kernel-based quantum algorithms to model the degradation patterns of robotic arm components. This enables early detection of potential failures and optimizes maintenance schedules with improved accuracy. The framework is validated using real-world data from aerospace robotic systems provided by EMBRAER, demonstrating superior performance in terms of accuracy, efficiency, and robustness compared to classical machine learning methods. Our results, supported by the collaboration with SENAI UpLab, highlight the potential of quantum-enhanced techniques, including QNNs and QSVCs, to revolutionize predictive maintenance in the aerospace industry, reducing downtime, minimizing costs, and enhancing operational safety. This study contributes to the growing body of research at the intersection of quantum computing and industrial applications, paving the way for future advancements in intelligent maintenance systems.