Enhancing Predictive Analytics in Industrial Robot Operations with Cycle-Consistent Encoding and Quantum Multi-Output Networks: A Control Theory-Inspired Solution
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.
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.
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 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.
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 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.