Smart grid stability prediction is crucial for providing uninterrupted power and high maintainability while operating smart grids. State-of-the-art research focuses on using Machine Learning (ML) to solve smart grid stability prediction; however, the complex nature of smart grids that serve thousands of users poses challenges for classical ML. Quantum Machine Learning (QML) has emerged as a possible solution to overcome these challenges, making QML a promising approach for energy applications. While QML shows promise in energy research, its application to grid stability problems remains unexplored. In this study, we propose a regularization-enhanced hybrid quantum-classical neural network that combines a classical encoder-decoder architecture with a Variational Quantum Circuit (VQC). Our model achieves 92.25 % accuracy, competitive with parameter-matched classical neural networks while outperforming Gradient Boosting and Random Forest by$3.0-4.25 \%$. Noise robustness analysis shows that the proposed hybrid model demonstrates a slight advantage under dropout noise while maintaining comparable performance under Gaussian and uniform noise, outperforming tree-based methods across all noise types.
Smart grid stability prediction is crucial for providing uninterrupted power and high maintainability while operating smart grids. State-of-the-art research focuses on using Machine Learning (ML) to solve smart grid stability prediction; however, the complex nature of smart grids that serve thousands of users poses challenges for classical ML. Quantum Machine Learning (QML) has emerged as a possible solution to overcome these challenges, making QML a promising approach for energy applications. While QML shows promise in energy research, its application to grid stability problems remains unexplored. In this study, we propose a regularization-enhanced hybrid quantum-classical neural network that combines a classical encoder-decoder architecture with a Variational Quantum Circuit (VQC). Our model achieves 92.25 % accuracy, competitive with parameter-matched classical neural networks while outperforming Gradient Boosting and Random Forest by3.0−4.25%. Noise robustness analysis shows that the proposed hybrid model demonstrates a slight advantage under dropout noise while maintaining comparable performance under Gaussian and uniform noise, outperforming tree-based methods across all noise types.
The stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data" dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid’s stability into "stable" and "unstable" categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general.
Non-Intrusive Load Monitoring (NILM) is an essential component of modern smart grid systems, enabling the disaggregation of total power consumption into appliance-level usage. Accurate appliance classification is critical for optimal power flow and uninterrupted grid operation. In state-of-the-art NILM applications, classical machine learning (ML) models require abundant labeled data. However, in real-world scenarios, limited data and measurement noise are common challenges. Quantum machine learning (QML), which leverages quantum mechanical properties, may offer advantages for NILM compared to classical ML. In this study, we propose QLID-Net (Quantum Load IDentification Network), a hybrid quantum-classical neural network for appliance classification in NILM. The proposed architecture consists of a classical encoder, a variational quantum circuit (VQC), and a classical decoder.We evaluated the model on a smart meter dataset (five appliance classes) across four experiments: data efficiency, classical/data noise robustness, baseline comparison, and quantum hardware noise robustness comparing it against XG-Boost, TabPFN, Random Forest, and a parameter-matched classical neural network. In the data efficiency experiment, the proposed model outperformed two classical models at 10 or fewer samples per class and significantly outperformed the parameter-matched classical neural network (p<0.001, Cohen’s d=1.80), with a crossover point at approximately 20 samples per class. Under additive noise (Gaussian, Uniform), the proposed model showed superior robustness, outperforming all classical baselines with up to 17.9% improvement over XGBoost and up to 21.3% improvement over TabPFN. In the baseline comparison, it achieved competitive performance with XGBoost and TabPFN (within 5.1%) and 100% training convergence compared to 70% for the classical neural network. Finally, in the quantum hardware noise tests, results showed that although QLID-Net can compete with the parameter matched classical neural network, it faces a performance decline. Our results identify practical regimes where quantum advantages are beneficial, especially for scenarios with limited data or measurement noise. This study contributes to the growing evidence of practical QML applications and paves the way for quantum-assisted hybrid models in NILM.
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.
The modernization of power grids, transitioning into smart grids, demands innovative technologies to manage and optimize energy distribution effectively. As power systems continue to evolve into smart grids with distributed and dynamic architectures, efficient load balancing becomes critical. Classical methods often prove inadequate in handling the complexity and variability of smart grid data. Increasing popularity of renewable energy sources adds another layer of complexity. The integration of renewable energy sources and increasing demand volatility pose significant challenges for smart grid load balancing. Traditional machine learning (ML) methods, while promising, face limitations in real-time optimization and scalability. Quantum Machine Learning (QML), which leverages quantum computing principles like superposition and entanglement, offers a promising frontier for optimizing energy distribution and load balancing by enhancing established ML algorithms. This paper surveys and compares current research on the application of QML to smart grid load balancing, exploring quantum neural networks (QNNs), quantum support vector machines (QSVM), as well as quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA), to improve various aspects of load-balancing efficiency in smart grids. By encoding classical grid data into quantum states, QML models exploit superposition and entanglement to process high-dimensional datasets faster than classical methods.
Quantum machine learning has recently emerged as a promising paradigm that leverages the expressive power of quantum circuits to address complex learning tasks. In this work, we investigate the applicability of hybrid quantum-classical neural networks to sentiment analysis, a central problem in natural language processing. We focus on a dataset of tweets related to COVID-19, where the textual content is vectorized using TF-IDF and fed into both classical feedforward networks and hybrid architectures incorporating parameterized quantum circuits. Our results show that hybrid models can achieve accuracy comparable to the classical baseline, while exhibiting distinct learning dynamics, especially in terms of validation loss and accuracy, that suggest a richer representational capacity. Moreover, when applying transfer learning to an SMS spam classification task, the hybrid models consistently outperform the classical counterpart, achieving an accuracy increase of 15 percentage points (from 66% to 81%) on the spam class, demonstrating enhanced generalization. These findings highlight the feasibility of employing QML for natural language processing and point toward the potential advantages of hybrid models as quantum hardware continues to advance.
Quantum Machine Learning (QML) has emerged as a transformative paradigm that integrates the computational advantages of quantum computing with the adaptive intelligence of machine learning to solve complex optimization problems in smart computing systems. Traditional optimization techniques often struggle with high-dimensional search spaces, combinatorial complexity, computational overhead, and scalability limitations in applications such as intelligent transportation, healthcare analytics, smart manufacturing, cybersecurity, cloud computing, and Internet of Things (IoT) infrastructures. QML approaches exploit quantum superposition, entanglement, and quantum parallelism to accelerate optimization tasks and enhance predictive performance beyond classical machine learning capabilities. This research proposes a hybrid Quantum Machine Learning optimization framework that combines Variational Quantum Circuits (VQCs), Quantum Neural Networks (QNNs), and classical deep learning models to address resource allocation, task scheduling, energy optimization, and intelligent decision-making in smart computing environments. The proposed framework integrates quantum-enhanced feature extraction, adaptive optimization layers, and reinforcement-based learning mechanisms to improve convergence speed and computational efficiency. Experimental analysis is performed using benchmark optimization datasets and simulated quantum environments to evaluate accuracy, execution time, energy efficiency, and scalability. Comparative results demonstrate that the proposed QML-based framework achieves superior optimization accuracy, reduced computational latency, and improved resource utilization when compared with traditional machine learning and classical metaheuristic optimization methods. Furthermore, the study highlights the practical significance of hybrid quantum-classical architectures in next-generation intelligent systems where large-scale optimization and real-time decision support are critical. The findings suggest that QML can significantly contribute to the advancement of sustainable, adaptive, and high-performance smart computing ecosystems while opening new directions for future quantum-aware intelligent infrastructures.