This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quantum Neural Networks (QNN), a novel integration of Quantum Parallel Multi-layer Monte Carlo (QPMC) with QAE proposed in this work, and Quantum-Inspired Evolutionary Optimization (QIEO) to optimize power extraction from photovoltaic (PV) panels and bladeless wind turbines (BI-WT). The analysis considers key parameters, including effective open-circuit voltage (VOC) of the series-connected PV modules ranging from 85.54V to 89.74V, short-circuit current (ISC) between 2.81A and 21.47A, wind speed (v) spanning from 0.6m/s to 22.3m/s, and solar irradiance (Gθ) varying from 119W/m2to 924W/m2. VQE and VQD optimize power output effectively, with VQE converging faster. QAOA remains stable but does not always reach optimal power values. QAE estimates power output with high accuracy, aligning with experimental data. QNN achieves lower validation loss when trained for 200 epochs, demonstrating improved generalization. The QPMC with QAE method estimates power output fluctuations with minimal deviation. QIEO exhibits enhanced optimization, with population sizes above 250 individuals leading to stable power output maximization.
This study compares seven quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quantum Neural Networks (QNN), a novel integration of Quantum Parallel Multi-layer Monte Carlo (QPMC) with QAE proposed in this work, and Quantum-Inspired Evolutionary Optimization (QIEO) to optimize power extraction from photovoltaic (PV) panels and bladeless wind turbines (BI-WT). The analysis considers key parameters, including effective open-circuit voltage (VOC) of the series-connected PV modules ranging from 85.54V to 89.74V, short-circuit current (ISC) between 2.81A and 21.47A, wind speed (v) spanning from 0.6m/s to 22.3m/s, and solar irradiance (Gθ) varying from 119W/m2to 924W/m2. VQE and VQD optimize power output effectively, with VQE converging faster. QAOA remains stable but does not always reach optimal power values. QAE estimates power output with high accuracy, aligning with experimental data. QNN achieves lower validation loss when trained for 200 epochs, demonstrating improved generalization. The QPMC with QAE method estimates power output fluctuations with minimal deviation. QIEO exhibits enhanced optimization, with population sizes above 250 individuals leading to stable power output maximization.
The researched data shows that quantum algorithms have been mostly used for battery’s chemistry and conventional grid systems. These algorithms have proved to be computationally cheaper as a common meritorious aspect in almost all implementations. However, usage of quantum algorithms to integrate two renewable energy systems is yet to be explored. This study investigates the application of quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Amplitude Estimation (QAE)—to optimize energy extraction from solar panels and bladeless wind turbines. Nine different optimizers are used on 3066 and 11547 datapoints of wind and solar respectively, to comment about optimality between QAOA, VQD and VQE. Results demonstrate that optimizers like NELDER_MEAD and COBYLA excel in convergence speed, whereas in case of energy minimization SPSA and SLSQP for VQE, COBYLA and P_BFGS for VQD have outperformed other optimizers. QAOA effectively tackles combinatorial optimization tasks as convergence speed for all optimizers has been relatively rapid. Since VQE, VQD and QAOA have not been accurate in finding optimal data point, hence QAE is utilized. Additionally, QAE’s precision in amplifying high-probability quantum states aligns well with the periodic and structured nature of wind and solar energy datasets.
The design and operation of renewable energy conversion systems—such as wind turbines, solar photovoltaic arrays, and hybrid storage networks—require balancing multiple, often conflicting objectives, including energy efficiency, cost minimization, environmental impact, and system reliability. Traditional optimization techniques, while effective for isolated parameters, struggle with the combinatorial complexity and nonlinear interdependencies inherent to integrated energy systems. This study explores the application of quantum optimization algorithms in multi-objective renewable energy design, emphasizing how quantum annealing, Quantum Approximate Optimization Algorithm (QAOA), and Variational Quantum Eigensolver (VQE) frameworks can accelerate solution discovery and improve design precision. By leveraging the principles of superposition and entanglement, quantum systems can process vast design spaces in parallel, enabling the rapid evaluation of trade-offs among competing objectives. A hybrid quantum–classical workflow is proposed to integrate quantum solvers into existing computational pipelines for system simulation, control optimization, and energy forecasting. Preliminary modeling results and conceptual simulations indicate that quantum-enhanced optimization not only reduces computation time but also achieves higher Pareto-optimal efficiency compared to conventional multi-objective genetic algorithms and particle swarm optimizers. This interdisciplinary approach demonstrates the transformative potential of quantum computation in sustainable energy system design, supporting the global transition toward carbon neutrality and energy equity. By embedding quantum optimization into renewable infrastructure development, the study provides a foundation for next-generation decision-making frameworks that harmonize performance, sustainability, and scalability.
Photovoltaic array topology optimization was shown to improve efficiency in renewable energy plants. Previous studies demonstrated improvements via simulation at the level of 7-12% or more. In this paper, we describe solar array topology optimization systems based on quantum machine learning algorithms. The idea of using quantum machine learning can be useful in cases where the objective is to optimize power output in large sites with several thousands of panels. We specifically propose and assess a quantum circuit for a neural network implementation for photovoltaic topology optimization. Results and comparisons are presented using classical and quantum neural network implementations. In addition, solar array topology optimization simulations and comparisons using a quantum neural network are described for different numbers of qubits.
The exponential runtime of quantum simulators on classical machines, along with long queue times and high costs associated with real quantum devices, presents significant challenges for the efficient optimization of Variational Quantum Algorithms (VQAs) such as the Variational Quantum Eigensolver (VQE), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Neural Networks (QNNs). To address these limitations, we propose a novel approach, DyPP (Dynamic Parameter Prediction), which accelerates the convergence of VQAs by leveraging regular trends in the parameter weights to update parameters efficiently. We introduce two techniques for optimal prediction performance: Naive Prediction (NaP) and Adaptive Prediction (AdaP). Through extensive experimentation and training of multiple QNN models on various datasets, we demonstrate that DyPP achieves a speedup of approximately 2.25×compared to standard training methods, while also providing improved accuracy (up to 2.3% higher) and lower loss (up to 6.1% reduction) with minimal storage and computational overhead. We further evaluate DyPP’s effectiveness in VQE for molecular ground-state energy estimation and in QAOA for graph MaxCut. Our results show that, on average, DyPP leads to a speedup of up to 3.1× for VQE and 2.91× for QAOA compared to traditional optimization, while requiring up to 3.3× fewer shots (i.e., repeated circuit executions). Even in the presence of hardware noise, DyPP outperforms existing optimization techniques, delivering up to 3.33× speedup and requiring 2.5× fewer shots, thereby enhancing the efficiency of VQAs.
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.
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.