The modern power systems have been reshaped into sophisticated, data-intensive ecosystems with high requirements in accurate and adaptive control of energy through rapid urban electrification and renewable integration. Patronizing the traditional deep and reinforcement learning models, despite their effectiveness in pattern recognition, are typically unable to represent high-dimensional dependencies and stochastic variations of large-scale smart grids. In order to overcome the preceding, the paper presents a Quantum-Enhanced Machine Learning (QEML) framework that combines the use of quantum feature encoding, hybridized variation circuits, and reinforcement-based optimization to oppose predictive sustainability and real-time energy control. The model maps correlated grid variables into quantum states using amplitude phase encoding and jointly trains a classical LSTM layer using adaptive gradient exchange which optimizes dynamically and has low computational latency. The evidence of experimental analysis indicates that QEML reduces the cost of prediction error by 35 percent and increases the energy-saving efficiency by 17 percent compared to the state-of-the-art approaches. The adaptive quantum kernel in the framework also improves a quicker convergence as well as lowers the amount of carbon footprint. This study provides QEML as a practical solution to the concept of quantum-intelligent, self-optimizing smart grids and the connection between the computational innovation and the sustainable energy transitions.
The modern power systems have been reshaped into sophisticated, data-intensive ecosystems with high requirements in accurate and adaptive control of energy through rapid urban electrification and renewable integration. Patronizing the traditional deep and reinforcement learning models, despite their effectiveness in pattern recognition, are typically unable to represent high-dimensional dependencies and stochastic variations of large-scale smart grids. In order to overcome the preceding, the paper presents a Quantum-Enhanced Machine Learning (QEML) framework that combines the use of quantum feature encoding, hybridized variation circuits, and reinforcement-based optimization to oppose predictive sustainability and real-time energy control. The model maps correlated grid variables into quantum states using amplitude phase encoding and jointly trains a classical LSTM layer using adaptive gradient exchange which optimizes dynamically and has low computational latency. The evidence of experimental analysis indicates that QEML reduces the cost of prediction error by 35 percent and increases the energy-saving efficiency by 17 percent compared to the state-of-the-art approaches. The adaptive quantum kernel in the framework also improves a quicker convergence as well as lowers the amount of carbon footprint. This study provides QEML as a practical solution to the concept of quantum-intelligent, self-optimizing smart grids and the connection between the computational innovation and the sustainable energy transitions.
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.
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.
In recent years, the electricity consumption in residential sector has witnessed a significant increase due to the population growth on one hand and the widespread adoption of electrical appliances on the other hand.Hence, finding solutions to decrease electricity consumption has become a matter of great interest for researchers.To address this challenge, we propose a Quantum Cognitive IoT (QCIoT) Framework that integrates quantum deep learning with edge computing to optimize energy use in smart homes.Our key innovation is a hybrid Quantum Long Short-Term Memory (QLSTM) model, which enhances traditional LSTM networks by leveraging quantum circuits for improved timeseries forecasting.Specifically, QLSTM employs parameterized quantum gates to process temporal dependencies more efficiently, enabling higher accuracy than classical approaches.We evaluate quantum-enhanced LSTMs (QLSTMs) against classical LSTM baselines on multivariate time-series forecasting.Experimental results demonstrate that QLSTMs significantly outperform classical counterparts, with the multivariate QLSTM (MQLSTM) achieving a 25.8% reduction in RMSE and improving explanatory power by 78.3%.While QLSTMs exhibit slightly slower convergence, they deliver superior generalization, evidenced by lower test loss and stable training dynamics.These advantages stem from quantum parallelism, entanglement and optimized state representation, which enable superior handling of noisy, high-dimensional smart home data.By integrating quantum-enhanced forecasting with edge-based IoT systems, our framework offers a scalable solution for real-time energy management in smart homes.This work bridges quantum computing and smart infrastructure, demonstrating practical benefits for sustainability and energy savings.
Use of AI/ML is associated with more and more computing power, which often necessitates the application to guzzle energy and take time to train. The paper presents a Quantum-Enhanced High-Performance Computing (Q-HPC) system that will combine traditional HPC units with quantum-assisted optimization to enhance the model training in addition to the predictive accuracy and energy efficiency. The framework that could be used to work with such large volumes of data is multi- GPU/CPU parallelization, and the optimization of parameters and hyperparameters can be implemented with the help of quantum-inspired algorithms. This will lead to a hybrid computation balancing dynamically the classical and quantum computations. Q-HPC was experimented with multiple AI/ML model types, such as convolutional networks, transformer models, graph neural networks and reinforcement learning agents, in which cases it was observed to traverse to the solution faster, more accurately and used less energy than the traditional HPC. It can also be said that the framework is dynamically adaptable and sustainable, i.e. it could be deployed to a massive diversifying range of tasks of AI/ML. The suggested model is a combination of the performance and scalability of the classical HPC with the optimization performance of quantum computing in order to create a new and valuable approach to the next generation AI. It deals with the issues of performance and environmental concerns of high-performance computing.
Quantum computing and artificial intelligence (AI) are positioned as groundbreaking tools capable of tackling complexities inherent to modern computation posed by high-dimensional optimization problems.This research presents a quantum-enhanced optimization framework that leverages the QAOA (Quantum Approximate Optimization Algorithm) and VQE (Variational Quantum Eigen solver) within a hybrid quantum-classical paradigm.Machine learning models, including reinforcement learning and NN (neural networks), are integrated to optimize the quantum circuit parameters, enhance convergence, and improve scalability for complex problem spaces.Experimental evaluations demonstrate that this approach outperforms traditional optimization techniques in terms of efficiency and solution accuracy, particularly for large-scale multi-objective tasks.The framework is applied to key domains such as smart energy systems, logistics, and scientific modeling, illustrating its versatility and alignment with sustainable development goals.This study also explores the limitations of current quantum hardware and discusses future pathways for advancing hybrid quantum AI systems, offering significant contributions to next generation computational methodologies and intelligent problem solving.
The increasing complexity of modern power grids, driven by the integration of renewable energy sources, decentralized energy generation, and dynamic load variations, necessitates advanced computational approaches for efficient grid optimization. Conventional algorithms often struggle with the high-dimensionality and computational intensity of power system optimization problems, prompting the exploration of quantum computing as a transformative alternative. Quantum computing, particularly quantum machine learning (QML) and quantum optimization algorithms, has demonstrated significant potential in accelerating grid stability analysis, energy dispatch, and fault detection. This chapter provides a comprehensive investigation into quantum computing applications for power electronics, focusing on quantum-inspired machine learning models and hybrid quantum-classical frameworks for IoT-enabled smart grids. The potential advantages of quantum algorithms, including the Quantum Approximate Optimization Algorithm (QAOA), Variational Quantum Eigensolver (VQE), and Grover’s search algorithm, are analyzed in the context of grid optimization, fault detection, and cybersecurity. Additionally, a comparative analysis of classical and quantum machine learning techniques highlights key performance differences and the challenges associated with practical quantum implementations. Despite the promising advancements, several challenges, including quantum hardware limitations, noise susceptibility, and scalability issues, must be addressed for real-world deployment. Future directions emphasize the need for noise-resilient quantum algorithms, efficient hybrid architectures, and quantum-inspired classical methods to bridge the gap between existing computational techniques and next-generation quantum solutions for power grid management.