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 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.
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 quality of raw materials is fundamental to the reliability and overall performance of final products, serving as the cornerstone of modern manufacturing standards. While traditional inspection methods can be effective, they are frequently time-consuming, labor-intensive, and unsuitable for high-throughput production environments. Recent advances in quantum computing offer significant advantages for processing large-scale data and complex feature representations, with strong potential to address the limitations of traditional inspection techniques. This study introduces an automated approach for grading raw materials, such as steel and fabric, which are widely used in industries including automotive, textiles, and general manufacturing, by integrating deep learning with quantum machine learning. A Hybrid Quantum Classical Neural Network (HQCNN) is proposed to enhance defect detection. The HQCNN utilizes a fine-tuned ResNet-18 backbone for robust feature extraction. Extracted features are used by a trainable depth controller that determines the complexity of a six-qubit variational quantum circuit (VQC) with data-adaptive, dynamic depth. Each VQC layer consists of rotations around the Y -axis (RY) and Z -axis (RZ), combined with Controlled-NOT (CNOT) gates arranged in a ring topology, with the total number of layers determined on a per-sample basis. The model is trained using a resource-aware loss function that penalizes excessive circuit depth, and the resulting quantum features are fused with classical features for final classification. Comprehensive ablation studies comparing the HQCNN against parameter-matched classical multi-layer perceptrons (MLPs) and fixed-depth quantum circuits demonstrate the fundamental advantage of the proposed approach. The dynamic HQCNN achieves 88.1 ± 2.4% accuracy on the complex metal surface defect dataset and 93.0 ± 1.8% on the fabric defects dataset. More importantly, it yields a statistically significant improvement in the macro F1-score (from 0.79 in classical parameter-matched baselines to 0.87), proving its superior capability in recognizing complex, minority-class defects while utilizing a fraction of the trainable parameters in the classification head compared to traditional architectures.
The rapid expansion of the Internet of Things in critical industrial environments has significantly increased the attack surface, exposing systems to sophisticated cyber threats. Traditional pattern-based intrusion detection systems struggle to detect such advanced attacks, while deep learning approaches, despite achieving high detection accuracy, often suffer from high computational cost and latency, limiting their deployment in resource-constrained edge gateways. Quantum machine learning offers a promising alternative by enabling high-dimensional feature representation; however, current implementations are constrained by hardware noise, limited qubit availability and backend-dependent execution characteristics in the Noisy Intermediate-Scale Quantum era. To address these challenges, this paper proposes a Residual Hybrid Quantum-Classical Neural Network (RHQ-CNN) for efficient intrusion detection in Edge-Industrial Internet of Things (Edge-IIoT) environments under idealized quantum simulation conditions. The proposed framework employs a classical neural network encoder to compress high-dimensional network traffic into a compact latent representation suitable for quantum processing. A variational quantum circuit with serial data re-uploading is then utilised to model complex non-linear decision boundaries without increasing qubit requirements. In addition, a residual connection fuses classical and quantum representations to improve training stability and preserve latent feature information. The model is evaluated on the Edge-IIoTset dataset and achieves a test accuracy of 99.94%, with high weighted performance across the 15-class detection task, although the extremely low-sample Fingerprinting class remains comparatively more challenging. Additional controlled ablation, deployment-cost, and bootstrap confidence interval analyses demonstrate the test-set metric stability and computational trade-offs of the proposed architecture. These findings highlight the potential of hybrid quantum-classical models for next-generation cybersecurity in industrial IoT systems.
Abstract Neuro-imaging data is necessary for timely and correct diagnosis of the disease Alzheimer’s (AD). Early clinical intervention is challenging due to the high-dimensional nature of the images, making it computationally difficult to work on representations, sparse annotated datasets and inter-subject variance. Deep convolutional VGG, ResNet, DenseNet, MobileNet and other neural networks (CNNs) have shown good performance. Diagnostic performance, but they are based on a high number of parameters and require highly trained personnel. Data can often times restrict resilience to data-skimy clinical settings. To address these challenges, the current paper presents a hybrid Quantum Machine Learning (QML) architecture for Alzheimer’s disease Detection and classification based on a combination of classical CNN-based feature extraction and Variational quantum classifier. In the suggested method, diseased, relevant, and compact features are extracted using pre-trained CNN backbones. Data from structural MRI scans is later translated into quantum states and computed. With parameterised variational quantum circuits optimised with a hybrid quantum-classical learning loop. The proposed method has been experimentally evaluated on benchmark datasets of MRI scans of Alzheimer’s patients. The hybrid CNNQML model has a mean classification accuracy of about 93% which is similar to deep CNN models, including ResNet and DenseNet (94%), and much more. Beating lightweight models like MobileNet and shallow CNNs by six percentage points. It is worth noting that the hybrid QML model performs better in conditions with limited training data. Performance degradation is observed in accuracy and AUC for all CNN variants, with up to 5% lower performance to deep CNNs. Cross-validation, confidence interval, paired significance test, and statistical validation. Effect size analysis proves the fact that the hybrid model produces statistically significant performance, as well as cases of clinically meaningful improvements over lightweight CNN baselines. Similar to the state-of-the-art deep networks with significantly reduced trainable parameters in the classification stage. These findings show that hybrid QML is a data-efficient and powerful substitute in the diagnosis of Alzheimer’s. On the whole, this is a work that sets a practical way to integrate quantum machine learning into clinical neuro-imaging pipelines and hybrid settings. QML has supplementary deep-learning enhancement to classics in next-generation Alzheimer’s Decision-support systems.
There is a pressing need for anomaly detection algorithms that can handle large amounts of diverse traffic data due to the quick growth of networked systems and the sophistication of contemporary cyberattacks. Despite the widespread use of conventional machine learning techniques for network intrusion detection, feature representation constraints, dataset imbalance, and the computational expense of scaling to vast problem spaces sometimes restrict their efficacy. In the meantime, new developments in quantum-assisted computation have opened up exciting possibilities for using quantum state changes to improve the feature learning process. Using the NF-UNSWNB15-v2 benchmark, this research presents HybridQCNN, a unique quantum-classical computing framework for binary classification of network traffic.The proposed architecture combines standard preprocessing techniques with an 8-qubit Parameterized Quantum Circuit (PQC) featuring multiple variational layers, where IsingXX and IsingYY entangling operations are applied to encode interfeature correlations, and a subsequent fully connected classical layer handles the final binary decision. A rigorous leakage-free data partitioning protocol was enforced, ensuring that all preprocessing transformations—including feature encoding, normalization, and threshold computation—were derived exclusively from the training subset, thereby preventing any information leakage from held-out partitions during evaluation. Experimental validation on a class-balanced subset of 60,000 traffic records yielded a classification accuracy of 97.33%, an F1-score of 0.9730, and a malicious traffic detection precision of 98.38%. Furthermore, the model achieved an AUC-ROC of 0.9861, reflecting strong class-level discriminability. Despite this performance, the entire model contains only 489 trainable parameters, underscoring the parameter efficiency of hybrid architectures relative to conventional deep learning baselines. These results suggest that integrating variational quantum circuits with classical inference modules provides an effective and scalable solution for network anomaly detection.
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.