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.
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.
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.
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.
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.
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.
Abstract Industrial robotic systems rely on predictive maintenance to prevent failures and optimize operational efficiency. However, accurately detecting faults such as protective stop and grip loss remains challenging due to high-dimensional sensor data and inherent noise. To address this, we propose a quantum multi-output network combined with cycle-consistent autoencoders, leveraging quantum feature encoding to enhance fault detection. The model effectively reduces dimensionality while mitigating quantum noise propagation, ensuring computational efficiency with fewer qubits. Our approach achieves 77% training accuracy and 76% validation accuracy, with F1 scores of 0.70 for protective stop and 0.68 for grip loss, significantly outperforming conventional deep learning models such as CNN-RNN (54.9%) and gradient boosting (67.6%). Compared to baseline multi-output networks, our quantum-enhanced framework provides a notable performance boost while maintaining robustness across large-scale datasets. By integrating quantum computing principles with structured sensor processing, our method demonstrates superior predictive capability for industrial fault detection. This study underscores the potential of hybrid quantum-classical architectures in industrial automation and sets the foundation for further advancements in quantum-enhanced predictive maintenance systems.
Background: Artificial Intelligence (AI) and Machine Learning (ML) have significantly transformed predictive analytics across domains such as healthcare, finance, and environmental sciences. However, traditional ML models face limitations when dealing with large-scale, high-dimensional datasets, particularly due to computational inefficiencies and the "curse of dimensionality." Quantum computing offers promising solutions by leveraging superposition and entanglement to perform complex computations at exponential speeds. Objective: This study aims to explore how quantum computing can enhance AI and ML models, particularly in the domain of predictive analytics. It proposes a comprehensive Quantum-AI framework that integrates quantum technologies into existing predictive systems to overcome the challenges posed by classical approaches. Methods: The study employed hybrid quantum-classical models using frameworks like Qiskit, TensorFlow Quantum, and Pennylane. Datasets were sourced from real-world applications in finance, healthcare, and climate science. Experiments were designed to compare classical and quantum- enhanced models on metrics such as accuracy, scalability, and computational time. Specific algorithms used include Quantum Support Vector Machines (QSVM) and Quantum Neural Networks (QNN). Result: The proposed quantum-enhanced models showed significant improvements: • Accuracy increased from 85% (classical) to 92% (quantum) in finance-related predictions. • Diagnostic models in healthcare achieved a 94% accuracy rate and reduced training time by 50%. • Climate modeling tasks achieved 90% improved accuracy and 20% faster simulation times. The results validate the effectiveness of quantum models in processing large, complex datasets more efficiently. Conclusion: Quantum-enhanced AI presents a transformative approach to predictive analytics by addressing the core limitations of classical ML systems. This study demonstrates the practical feasibility and industrial relevance of integrating quantum computing into AI workflows. It opens avenues for further research in developing scalable, domain-specific quantum algorithms and hybrid AI models for real-world applications.