Smart grids form the backbone of modern energy infrastructures. They require accurate power prediction to ensure efficient energy management and maintain grid stability. Recent advancements in quantum computing have provided motivation to explore quantum machine learning models for data driven prediction task. In this study, a quantum computing-based power prediction model is investigated using a Variational Quantum Circuit (VQC) to estimate the AC power output in a solar-based smart grid system. The proposed model is studied using a simulation software created using Qiskit framework and real-world solar dataset is used as input. To enable a holistic comparison, the classical regression models, including Linear Regression, Ridge Regression, Random Forest, and XGBoost, are used as baseline models. Experimental findings indicate that classical models have lower prediction errors and much faster execution times. Conversely, the quantum model can only capture the broad patterns of the data because of the constraints that relate to Noisy Intermediate-Scale Quantum (NISQ) devices such as circuit noise, limited qubit count, and optimization limitations. Furthermore, a multi-criteria evaluation is conducted to compare the classical and quantum approaches based on prediction accuracy, execution time, model complexity, and scalability. The findings provide valuable insights into the feasibility of applying quantum computing techniques to smart grid prediction tasks and highlight both the potential and the limitations of quantum machine learning for real-world energy systems.
Smart grids form the backbone of modern energy infrastructures. They require accurate power prediction to ensure efficient energy management and maintain grid stability. Recent advancements in quantum computing have provided motivation to explore quantum machine learning models for data driven prediction task. In this study, a quantum computing-based power prediction model is investigated using a Variational Quantum Circuit (VQC) to estimate the AC power output in a solar-based smart grid system. The proposed model is studied using a simulation software created using Qiskit framework and real-world solar dataset is used as input. To enable a holistic comparison, the classical regression models, including Linear Regression, Ridge Regression, Random Forest, and XGBoost, are used as baseline models. Experimental findings indicate that classical models have lower prediction errors and much faster execution times. Conversely, the quantum model can only capture the broad patterns of the data because of the constraints that relate to Noisy Intermediate-Scale Quantum (NISQ) devices such as circuit noise, limited qubit count, and optimization limitations. Furthermore, a multi-criteria evaluation is conducted to compare the classical and quantum approaches based on prediction accuracy, execution time, model complexity, and scalability. The findings provide valuable insights into the feasibility of applying quantum computing techniques to smart grid prediction tasks and highlight both the potential and the limitations of quantum machine learning for real-world energy systems.
Accurate load forecasting is critical for effective energy management, infrastructure planning, and ensuring the stability of modern power systems. With growing interest in Quantum Computing (QC), recent efforts have demonstrated the applicability of Quantum Machine Learning (QML) across various domains, including supervised and unsupervised learning. However, the use of QML models for Time Series Forecasting (TSF) remains relatively underexplored. In this work, we present a complete framework for TSF based on Variational Quantum Algorithms (VQAs), capable of both single-step and multi-step ahead forecasting. We use tensor product encoding to encode the classical input samples into quantum states, and use the data re-uploading strategy to introduce temporal dependencies across each layer of the quantum circuit. Within this framework, we systematically evaluate multiple Variational Quantum Circuit (VQC) architectures to assess their suitability for forecasting problem. Among the tested configurations, thenLocaland its modified variantQuReUpconsistently outperformed other quantum ansatz designs. Notably, in recursive multi-step forecasting scenarios, these models not only matched but surpassed the performance of a classical Artificial Neural Network (ANN). For example, thenLocalmodel achieved a 8% reduction in mean absolute error (MAE) over ANN for a 24-hour horizon. These results underscore the potential of VQAs for efficient and accurate TSF, particularly in scenarios requiring short to medium-term predictions. The proposed framework offers a promising foundation for future research, including extensions to diverse datasets and deployment on real quantum hardware to study the impact of quantum noise and scalability.
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.
Short-term solar irradiance prediction is critical for efficient power scheduling, the design of solar-integrated energy management systems, and maintaining grid stability. However, the inherent variability of solar energy introduces fluctuations in power output, and posing challenges for reliable grid operation. Leveraging recent advancements in quantum computing, the current work proposes a hybrid quantum-driven neural network for solar irradiance prediction, combining successive variational mode decomposition (SVMD) with an improved quantum long short-term memory (IQLSTM) network. To enhance circuit expressibility, a robust variational quantum circuit (RVQC) incorporating controlled-X (CRX) gates is embedded within the quantum long short-term memory (QLSTM) architecture. In the RVQC, a circular topology with two-way connections is employed to optimize qubit interconnections and strengthen entanglement. The proposed model is evaluated using two solar irradiance datasets of Hyderabad (2019) and Bengaluru (2019, 2018), obtained from the National Renewable Energy Laboratory (NREL). To assess the effectiveness of the proposed circuit, evaluations are carried out under varying weather conditions and across multiple forecasting horizons, and results are compared against classical LSTM and other quantum-based networks like quantum gate recurrent units (QGRUs), quantum support vector machine (QSVM) and QLSTM. Furthermore, the model is evaluated under noisy quantum environments to approximate realistic Noisy Intermediate-Scale Quantum (NISQ) hardware conditions, enabling the assessment of its robustness under practical quantum computing constraints. Experimental results demonstrate that the proposed model achieves superior predictive accuracy and robustness compared with existing classical and quantum-based approaches.
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.
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.
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.