Modern renewable power operations can be enhanced by integrating deep neural networks, particularly for forecasting solar irradiance. Recent advancements in quantum computing have shown potential improvements in classical deep neural networks. However, current challenges with quantum hardware, such as susceptibility to noise and decoherence, pose risks to its practicality. Hybrid quantum neural networks (HQNNs) are found to mitigate these issues, especially when integrated with graphics processing unit (GPU)-based pipelines. This paper presents a comparative study of different software platforms for developing HQNNs, using multi-location very short-term solar irradiance forecasting as an example. A classical benchmark model is initially designed based on statistical analysis of a 10-minute resolution solar irradiance dataset, with its parameters further optimized using Bayesian Optimization. The experimental design of this paper includes a loss comparison between classical neural networks and HQNNs across different seasons and a performance comparison between Pennylane, Torchquantum, and CUDA Quantum (CUDA-Q) as HQNN development platforms. Experimental results show that HQNNs achieve up to a 92.30% improvement in testing loss compared to classical neural networks. Regarding HQNN development platforms, Pennylane shows an 81.54% testing loss reduction from classical models, Torchquantum shows a 90.34% improvement, and CUDA-Q shows a 92.30% improvement in testing loss. Implementing hardware acceleration libraries for GPU-based state vector simulation demonstrates an approximate 275% speedup in average latency per epoch, a 218% speedup in inference time, and a 10.20% improvement in testing loss compared to CPU-based simulations. CUDA-Q achieves a training time 2.7 times shorter and an inference time 2.9 times shorter compared to Pennylane, while it is 32.3 times faster in training and 31 times faster in inference compared to Torchquantum.
Modern renewable power operations can be enhanced by integrating deep neural networks, particularly for forecasting solar irradiance. Recent advancements in quantum computing have shown potential improvements in classical deep neural networks. However, current challenges with quantum hardware, such as susceptibility to noise and decoherence, pose risks to its practicality. Hybrid quantum neural networks (HQNNs) are found to mitigate these issues, especially when integrated with graphics processing unit (GPU)-based pipelines. This paper presents a comparative study of different software platforms for developing HQNNs, using multi-location very short-term solar irradiance forecasting as an example. A classical benchmark model is initially designed based on statistical analysis of a 10-minute resolution solar irradiance dataset, with its parameters further optimized using Bayesian Optimization. The experimental design of this paper includes a loss comparison between classical neural networks and HQNNs across different seasons and a performance comparison between Pennylane, Torchquantum, and CUDA Quantum (CUDA-Q) as HQNN development platforms. Experimental results show that HQNNs achieve up to a 92.30% improvement in testing loss compared to classical neural networks. Regarding HQNN development platforms, Pennylane shows an 81.54% testing loss reduction from classical models, Torchquantum shows a 90.34% improvement, and CUDA-Q shows a 92.30% improvement in testing loss. Implementing hardware acceleration libraries for GPU-based state vector simulation demonstrates an approximate 275% speedup in average latency per epoch, a 218% speedup in inference time, and a 10.20% improvement in testing loss compared to CPU-based simulations. CUDA-Q achieves a training time 2.7 times shorter and an inference time 2.9 times shorter compared to Pennylane, while it is 32.3 times faster in training and 31 times faster in inference compared to Torchquantum.
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.
The intermittent nature of wind speed poses challenges for its widespread utilization as an electrical power generation source. As the integration of wind energy into the power system increases, accurate wind speed forecasting becomes crucial. The reliable scheduling of wind power generation heavily relies on precise wind speed forecasts. This paper presents an extended work that focuses on a hybrid model for 24 h ahead wind speed forecasting. The proposed model combines residual Long Short-Term Memory (LSTM) and a quantum neural network that is studied by a quantum simulator, leveraging the support of NVIDIA Compute Unified Device Architecture (CUDA). To ensure the desired accuracy, a comparative analysis is conducted, examining the qubit count and quantum circuit depth of the proposed model. The execution time required for the model is significantly reduced when the GPU incorporates CUDA, accounting for only 8.29% of the time required by a classical CPU. In addition, different quantum embedding layers with various entangler layers in the quantum neural network are explored. The simulation results utilizing an offshore wind farm dataset demonstrate that the proper number of qubits and embedding layer can achieve favorable 24 h ahead wind speed forecasts.
Abstract. Smart grid infrastructures are becoming increasingly vulnerable to a range of physical and cyber-physical faults that can result in outages, equipment damage, and safety hazards. This paper presents a comparative evaluation of a hybrid quantum-classical neural network (HQNN) against a classical artificial neural network (ANN) for binary fault detection in a simulated smart-grid environment. Both models are trained and tested on a 4,965-sample dataset derived from a power-system simulator jointly developed by Mississippi State University and Oak Ridge National Laboratory. We encode input features into quantum states using Hadamard and RY rotations, employ entangling layers in PennyLane, and fuse the resulting quantum expectation values with classical fully connected layers. The ANN baseline, in contrast, utilizes two ReLU-activated hidden layers and dropout. Performance is measured in terms of precision, recall, F1-score, ROC AUC, and CPU-only inference latency, with 5-fold cross-validation implemented. Results show that the HQNN got a higher ROC AUC (0.7565 vs. 0.6763), higher precision (80% vs. 69%), recall (61% vs. 54%), and F1-Score (65% vs. 58%), while the ANN achieves a lower average inference time that runs almost 65x faster per sample. These findings demonstrate that hybrid quantum-classical models can enhance discriminative power for sustainable, low-power smart-grid fault detection, even in resource-limited settings, at the cost of slightly increased inference time.
Abstract Hyperparameter Optimization of neural networks is a computationally expensive procedure, which requires a large number of different model configurations to be trained. To reduce such costs, this work presents a distributed, hybrid workflow, that runs the training of the neural networks on multiple Graphics Processing Units (GPUs) on a classical supercomputer, while predicting the configurations' performance with Quantum Support Vector Regression on a Quantum Annealer (QA). The workflow is shown to run on up to 50 GPUs and a QA at the same time, completely automating the communication between the classical and the quantum system. The approach is evaluated extensively on several benchmarking datasets from the Computer Vision, High Energy Physics, and Natural Language Processing domains. Results show that resource savings of up to approximately 9% can be achieved while obtaining similar, and in some cases even better, accuracy, highlighting the potential of hybrid quantum-classical machine learning algorithms. The workflow code is made available open-source to foster adoption in the community.
Modern progress in computing power provides great capacity to train large machine-learning models for sufficient accuracy demanded by commercial products. Commercial products such as autonomous vehicles are demanding the complexity of neural networks, traditionally increasing model size to achieve high accuracy and low loss to produce intelligent models. Meanwhile, Quantum Computing has recently advanced into the NISQ (Noisy Intermediate-scale Quantum) era, offering a solid foundation for the potential of improving modern computation tasks with quantum algorithms. Previous quantum algorithms (Variational Quantum Circuits) harness the random nature of quantum mechanics to preserve quantum information and improve model training by allowing parallel computing. This paper demonstrates a hybrid methodology implementing a Hybrid Quantum Neural Network (HQNN) to achieve quantum advantage for a convolutional regression model. The resulting hybrid model performance shows an immediate reduction in model complexity and improvement in training efficiency for the autonomous robot, this contrast with the classical control model provides a promising approach for the near-term feasibility of quantum-enhanced ML products for AI industries.
Time series forecasting involves analyzing historical data to predict future trends and is useful for reducing uncertainty in various phenomena. Recurrent Neural Network-based Long Short-Term Memory (LSTM) has been shown to be used for time series forecasting. However, the introduction of a hybrid quantum-classical model of LSTM, i.e., Quantum LSTM (QLSTM), opens up avenues for faster and more efficient computing and, therefore, is of significant potential. These potentials underscore the necessity for a comparative analysis of both models to assess their effectiveness and efficiency. This study evaluates the performance of QLSTM and classical LSTM models in time series forecasting. Through controlled parametric numerical experiments, the study examines the performance of both models using CPUs and GPUs in cloud environments. Our findings reveal significant improvements in the QLSTM model, with enhanced training accuracy and reduced test loss compared to the classical LSTM under similar epochs and batch sizes. The results demonstrate the superior efficiency of the QLSTM model in handling large, complex data sets compared to the classical LSTM model.