Insufficient experimental cycles, costly material screening, and ineffective solar-material synthesis all slow the rapid growth of photovoltaic nanostructures. Conventional machine learning algorithms and traditional trial-and-error techniques are inadequate in capturing the quantum-scale interactions that govern nano-engineered solar absorbers, resulting in suboptimal energy conversion efficiency and delayed commercialization. This study proposes a Nano-Quantum Variational Diffusion Model (NQVDM) to expedite the identification and optimization of photovoltaic nanomaterials, potentially addressing these challenges. The proposed framework employs bandgap energy, surface morphology, defect density, lattice structure, and variational quantum circuits to model high-dimensional synthesis parameters. The diffusion model iteratively improves the candidate material distributions until they reach high-efficiency configurations. The quantum variational layer, on the other hand, uses a hybrid quantum-classical optimization technique to capture nonlinear quantum correlations. An experimental evaluation was conducted using a reference dataset of photovoltaic nanostructure parameters, which had been validated both theoretically and experimentally. The NQVDM outperforms standard optimization methods like DNNs and GANs, as well as Bayesian optimization, in terms of stability and generating power. The results reveal that the synthesis optimization error decreased by 31.2%, the expected power conversion efficiency (PCE) increased by 22.4%, and the material discovery speed increased by 28.6%. Also, the time required for the computer to converge was reduced by 26.8%, suggesting that the diffusion-driven quantum framework might speed up processes. The proposed NQVDM significantly enhances the efficiency of solar material production by integrating quantum variational learning with probabilistic diffusion modeling. This paradigm enables data-driven, quantum-aware nanostructure design, enhancing photovoltaic performance measures while reducing experimental overhead. This technology is a strategy to move forward, scalable for identifying smart solar materials for the next generation.
Insufficient experimental cycles, costly material screening, and ineffective solar-material synthesis all slow the rapid growth of photovoltaic nanostructures. Conventional machine learning algorithms and traditional trial-and-error techniques are inadequate in capturing the quantum-scale interactions that govern nano-engineered solar absorbers, resulting in suboptimal energy conversion efficiency and delayed commercialization. This study proposes a Nano-Quantum Variational Diffusion Model (NQVDM) to expedite the identification and optimization of photovoltaic nanomaterials, potentially addressing these challenges. The proposed framework employs bandgap energy, surface morphology, defect density, lattice structure, and variational quantum circuits to model high-dimensional synthesis parameters. The diffusion model iteratively improves the candidate material distributions until they reach high-efficiency configurations. The quantum variational layer, on the other hand, uses a hybrid quantum-classical optimization technique to capture nonlinear quantum correlations. An experimental evaluation was conducted using a reference dataset of photovoltaic nanostructure parameters, which had been validated both theoretically and experimentally. The NQVDM outperforms standard optimization methods like DNNs and GANs, as well as Bayesian optimization, in terms of stability and generating power. The results reveal that the synthesis optimization error decreased by 31.2%, the expected power conversion efficiency (PCE) increased by 22.4%, and the material discovery speed increased by 28.6%. Also, the time required for the computer to converge was reduced by 26.8%, suggesting that the diffusion-driven quantum framework might speed up processes. The proposed NQVDM significantly enhances the efficiency of solar material production by integrating quantum variational learning with probabilistic diffusion modeling. This paradigm enables data-driven, quantum-aware nanostructure design, enhancing photovoltaic performance measures while reducing experimental overhead. This technology is a strategy to move forward, scalable for identifying smart solar materials for the next generation.
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.
Abstract The design of advanced materials for applications in areas of photovoltaics, energy storage, and structural engineering has made significant strides. However, the rapid proliferation of candidate materials—characterized by structural complexity that complicates the relationships between features—presents substantial challenges in manufacturing, fabrication, and characterization. This review introduces a comprehensive methodology for materials design using cutting‐edge quantum computing, with a particular focus on quadratic unconstrained binary optimization (QUBO) and quantum machine learning (QML). We introduce the loop framework for QUBO‐empowered materials design, including constructing high‐quality datasets that capture critical material properties, employing tailored computational methods for precise material modeling, developing advanced figures of merit to evaluate performance metrics, and utilizing quantum optimization algorithms to discover optimal materials. In addition, we delve into the core principles of QML and illustrate its transformative potential in accelerating material discovery through a range of quantum simulations and innovative adaptations. The review also highlights advanced active learning strategies that integrate quantum artificial intelligence, offering a more efficient pathway to explore the vast, complex material design space. Finally, we discuss the key challenges and future opportunities for QML in material design, emphasizing their potential to revolutionize the field and facilitate groundbreaking innovations.
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 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.
Generative models have achieved remarkable success in data synthesis, though recent advances driven by increasing model scale have introduced challenges in computational cost and efficiency. Quantum machine learning offers a promising alternative, representing complex data distributions using compact, highly expressive models. Here, we propose QDiffusion-TS, the first quantum generative diffusion model for time series synthesis, and validate it on the IQM quantum processor. The framework extends a classical diffusion architecture by replacing feed-forward components within the denoising transformer with quantum neural networks, yielding a hybrid quantum transformer that reduces the number of trainable parameters in each replaced component by nearly three orders of magnitude. Evaluated on financial time series from Apple and Amazon, the model generates synthetic data that more accurately reproduces the real distributions, reducing Wasserstein distance by approximately 44% relative to its classical counterpart across both datasets. In a downstream forecasting task, augmentation with the generated data improves predictive performance by up to 71% in RMSE over a baseline trained solely on real data. These results show that quantum enhanced architectures can consistently match and frequently surpass classical performance with substantially fewer parameters, establishing a practical framework towards more efficient and scalable data-driven generative modelling.
Quantum-driven lattice parameter optimization is a novel approach to materials science that leverages the power of quantum computing to efficiently explore the parameter space of crystal structures. Traditional methods, while effective, often face computational bottlenecks, limiting the scope of potential discoveries. This paper proposes a quantum-enhanced optimization strategy utilizing quantum annealing and variational quantum eigensolver (VQE) techniques to systematically search for optimal lattice parameters, potentially leading to the identification of novel material properties. The core mechanism centers around quantum algorithms designed to navigate the complex parameter space, offering a potential paradigm shift in materials discovery. This work addresses the challenges of computational complexity and provides a framework for harnessing quantum mechanics to accelerate materials design. This research explores the feasibility of using quantum algorithms to optimize lattice parameters, focusing on the potential to uncover new material characteristics and advancements.