Abstract. Smart grid infrastructures are becoming increasingly vulnerable to a range of physical and cyber-physical faults that can result in outages, equipment damage, and safety hazards. This paper presents a comparative evaluation of a hybrid quantum-classical neural network (HQNN) against a classical artificial neural network (ANN) for binary fault detection in a simulated smart-grid environment. Both models are trained and tested on a 4,965-sample dataset derived from a power-system simulator jointly developed by Mississippi State University and Oak Ridge National Laboratory. We encode input features into quantum states using Hadamard and RY rotations, employ entangling layers in PennyLane, and fuse the resulting quantum expectation values with classical fully connected layers. The ANN baseline, in contrast, utilizes two ReLU-activated hidden layers and dropout. Performance is measured in terms of precision, recall, F1-score, ROC AUC, and CPU-only inference latency, with 5-fold cross-validation implemented. Results show that the HQNN got a higher ROC AUC (0.7565 vs. 0.6763), higher precision (80% vs. 69%), recall (61% vs. 54%), and F1-Score (65% vs. 58%), while the ANN achieves a lower average inference time that runs almost 65x faster per sample. These findings demonstrate that hybrid quantum-classical models can enhance discriminative power for sustainable, low-power smart-grid fault detection, even in resource-limited settings, at the cost of slightly increased inference time.
Abstract. Smart grid infrastructures are becoming increasingly vulnerable to a range of physical and cyber-physical faults that can result in outages, equipment damage, and safety hazards. This paper presents a comparative evaluation of a hybrid quantum-classical neural network (HQNN) against a classical artificial neural network (ANN) for binary fault detection in a simulated smart-grid environment. Both models are trained and tested on a 4,965-sample dataset derived from a power-system simulator jointly developed by Mississippi State University and Oak Ridge National Laboratory. We encode input features into quantum states using Hadamard and RY rotations, employ entangling layers in PennyLane, and fuse the resulting quantum expectation values with classical fully connected layers. The ANN baseline, in contrast, utilizes two ReLU-activated hidden layers and dropout. Performance is measured in terms of precision, recall, F1-score, ROC AUC, and CPU-only inference latency, with 5-fold cross-validation implemented. Results show that the HQNN got a higher ROC AUC (0.7565 vs. 0.6763), higher precision (80% vs. 69%), recall (61% vs. 54%), and F1-Score (65% vs. 58%), while the ANN achieves a lower average inference time that runs almost 65x faster per sample. These findings demonstrate that hybrid quantum-classical models can enhance discriminative power for sustainable, low-power smart-grid fault detection, even in resource-limited settings, at the cost of slightly increased inference time.
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.
The increasing adoption of blockchain technology has presented significant challenges in maintaining the security and reliability of smart contracts. This study addresses the problem of identifying security flaws in smart contracts, which may result in monetary damages and diminished confidence in blockchain systems. A Hybrid Quantum–Classical Deep Neural Network (HQCDNN) approach was proposed, combining quantum computing principles with classical deep learning methods to identify various vulnerability types, including access control, arithmetic, front-running, reentrancy, time manipulation, denial of service, and unchecked low calls. The SmartBugs Wild Dataset was used for training, with TF-IDF employed as a preprocessing technique optimized for hybrid architectures. Experiments were conducted using hybrid architectures with 2-qubit and 4-qubit quantum layers, alongside a classical deep neural network (DNN) model for comparative analysis. The HQCDNN model attained accuracy levels ranging from 96.4% to 78.2% and F1-scores between 96.6% and 80.2%, showcasing enhanced performance compared to the classical and deep learning models referenced in the literature. These results highlight the capability of HQCDNNs to improve the identification of security flaws in smart contracts. Future work could focus on evaluating the model on actual quantum devices and expanding its application to larger datasets for further validation.
Reliable anomaly detection in subsea infrastructure is critical, as overlooked faults may result in severe environmental damage and substantial financial losses. Although classical machine learning approaches provide effective solutions, they often require significant computational resources and may struggle to capture the complex, high-dimensional correlations present in multivariate sensor data. In this work, we present a comprehensive evaluation of quantum and hybrid quantum–classical architectures using the 3W dataset, a large-scale industrial benchmark comprising 2,184 real and simulated subsea events. We develop an end-to-end pipeline that transforms raw multivariate time-series signals into quantum-compatible feature representations. Four hybrid architectures are systematically compared against six classical baselines in both supervised and unsupervised settings. Our results demonstrate that a compact hybrid model (Hybrid-A) achieves an F1-score of 0.9737 using only 177 trainable parameters, matching the performance of a 10,000-parameter Random Forest and exhibiting substantially higher parameter efficiency than conventional neural networks. In the unsupervised scenario, the proposed Quantum Autoencoder outperforms its classical counterpart, improving F1-score by 5.35% and ROC-AUC by 15.28% while reducing parameter count by 42%. These findings indicate that quantum-enhanced models can achieve competitive performance with significantly reduced model complexity, highlighting their potential for deployment in resource-constrained, safety-critical edge environments. Keywords: Quantum Machine Learning, Industrial Anomaly Detection, Hybrid Quantum-Classical Models, 3W Dataset, Edge Deployment, Parameter Efficiency
Reliable anomaly detection in subsea infrastructure is critical: overlooked faults can lead to catastrophic environmental damage and billions in losses. While classical machine learning offers a partial solution, it often demands substantial computational resources and struggles with the complex, high-dimensional correlations inherent in sensor data. This study presents the first comprehensive evaluation of quantum and hybrid quantum-classical architectures on the 3W dataset, a massive industrial benchmark of 2,184 real and simulated subsea events. We develop an end-to-end pipeline that transforms raw multivariate time series into quantum-ready features, comparing four hybrid architectures against six classical baselines. Our results are compelling: a simple hybrid model (Hybrid-A) achieves an F1 score of 0.9737 with just 177 parameters-matching the performance of a 10,000-parameter Random Forest while being 56× more parameter-efficient than standard neural networks. In the unsupervised setting, our Quantum Autoencoder significantly outperforms its classical counterpart, improving F1 by 5.35% and ROC-AUC by 15.28% with 42% fewer parameters. These findings demonstrate that quantum-enhanced models can deliver state-of-the-art performance with a fraction of the complexity, making them ideal for resource-constrained edge deployment in safety-critical systems.
With increasing prevalence of distributed energy resources (DERs), the growing threat of cyber-physical attacks, and the need for real-time optimization present major challenges for modern smart grids. With challenges also come opportunities. Although classical machine learning (ML) models, such as deep neural networks (DNNs), have shown promise toward meeting some of these challenges, they often struggle with the computational complexity when vast volumes of grid data are involved. The problems are particularly acute in situations when real-time responses are essential. This paper proposes a framework that integrates classical and quantum neural networks (QNNs) to initiate a new paradigm of time-sensitive grid management. We explore the potential of quantum computing to enhance the capabilities of classical neural networks in two critical applications: (1) real-time anomaly detection for cybersecurity and (2) dynamic optimal power flow (OPF) management for grid stability. We propose a hybrid quantum-classical neural network framework suitable for near-term quantum devices, discuss the theoretical advantages in computational speed and model capacity, and address the significant challenges related to quantum hardware noise, algorithmic design, and data encoding. The framework is flexible enough to allow for futureproofing as QNNs and quantum computing hardware continue to evolve. Our analysis suggests that QNNs, even in their current nascent stage, hold great promise to revolutionize smart grid analytics by solving complex, time-sensitive classification and optimization problems that are currently difficult or intractable for classical methods. The study therefore lays the groundwork for a future smart grid that is more secure, efficient, and robust.
Cyber attacks on data servers and critical infrastructure of fifth-generation (5G) wireless communication networks are increasing day by day. Therefore, an intelligent, reliable and cost effictive security of sixth-generation (6G) wireless communication network is inevitable. Quantum technology is one of the enabling technologies of future computing, as well as of the 6G. In the event of a cyberattack, rapid response is critical to minimize the risk of data loss and denial-of-service (DoS) attacks. Quantum computers are a new generation of computers that are expected to be much faster than classical computers. In this study, we have proposed and implemented a hybrid quantum neural network (HQNN) model. The proposed HQNN model was trained and tested on a dataset from the Australian center for cybersecurity. Simulation results show that the proposed HQNN model is faster in training as well as in the testing phase compared to classical neural networks. Moreover, our proposed model helps to overcome the underutilization of centeral processing unit (CPU) resources. CPU utilization of our proposed HQNN model is 95-100%, while that of the classical model is only 35-75% during the training and testing of the dataset.