The rising environmental cost of deep learning has placed Green AI, which promotes focus on reducing the carbon footprint of AI, at the forefront of sustainable computing. In this study, we investigate Quantum Machine Learning (QML) as a novel and energy-efficient alternative by benchmarking two quantum models, the Quantum Neural Network (QNN) and Quantum Long Short-Term Memory (QLSTM), on the N-BaIoT anomaly detection dataset. Our first phase of experiments compares the QNN and QLSTM models using ten distinct quantum circuit designs (ansätze A1-A10). We systematically compare trade-offs between classification performance, model complexity, training time, and energy consumption. The results indicate that simpler QNN ansätze can achieve accuracy comparable to more complex ones while consuming significantly less energy and converging faster. In particular, QNN with ansatz A4 provided the optimal balance between performance and energy efficiency, consistently outperforming QLSTM across most metrics. A detailed energy breakdown confirmed GPU usage as the dominant source of power consumption, underscoring the importance of circuit-efficient quantum design. To contextualize QML's viability, we conducted a second phase of experiments comparing quantum models with three benchmark classical machine learning models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and CatBoost. We find that the classical models demonstrated faster training times and lower energy consumption, highlighting and contrasting the maturity of algorithmic development that classical ML algorithms have already seen. Finally, we examined the energy implications of developing quantum models on actual quantum hardware. This third phase of experiments compared training on IBM Qiskit's emulation environment (running on GPU servers) versus execution on real IBM Quantum hardware. Highlighting the significant differences in execution time and energy footprint, extrapolated results indicate that quantum hardware still incurs higher energy costs. This suggests that further hardware-aware ansätz optimization and improvements in quantum infrastructure are essential to realizing carbon-efficient QML at scale.
The rising environmental cost of deep learning has placed Green AI, which promotes focus on reducing the carbon footprint of AI, at the forefront of sustainable computing. In this study, we investigate Quantum Machine Learning (QML) as a novel and energy-efficient alternative by benchmarking two quantum models, the Quantum Neural Network (QNN) and Quantum Long Short-Term Memory (QLSTM), on the N-BaIoT anomaly detection dataset. Our first phase of experiments compares the QNN and QLSTM models using ten distinct quantum circuit designs (ansätze A1-A10). We systematically compare trade-offs between classification performance, model complexity, training time, and energy consumption. The results indicate that simpler QNN ansätze can achieve accuracy comparable to more complex ones while consuming significantly less energy and converging faster. In particular, QNN with ansatz A4 provided the optimal balance between performance and energy efficiency, consistently outperforming QLSTM across most metrics. A detailed energy breakdown confirmed GPU usage as the dominant source of power consumption, underscoring the importance of circuit-efficient quantum design. To contextualize QML's viability, we conducted a second phase of experiments comparing quantum models with three benchmark classical machine learning models: Artificial Neural Network (ANN), Long Short-Term Memory (LSTM), and CatBoost. We find that the classical models demonstrated faster training times and lower energy consumption, highlighting and contrasting the maturity of algorithmic development that classical ML algorithms have already seen. Finally, we examined the energy implications of developing quantum models on actual quantum hardware. This third phase of experiments compared training on IBM Qiskit's emulation environment (running on GPU servers) versus execution on real IBM Quantum hardware. Highlighting the significant differences in execution time and energy footprint, extrapolated results indicate that quantum hardware still incurs higher energy costs. This suggests that further hardware-aware ansätz optimization and improvements in quantum infrastructure are essential to realizing carbon-efficient QML at scale.
With the advent of quantum computing, researchers have explored the applicability and any potential advantages of quantum algorithms. This study investigates the application of Quantum Machine Learning (QML) models for regression tasks. Utilizing two distinct CERN datasets (Dielectron events and Proton collision), we investigate prediction accuracy using two QML algorithms, namely Quantum Neural Network (QNN) and Quantum Long Short-Term Memory (QLSTM). We also discuss the comparative analysis and computational efficiency of QNN and QLSTM compared to classical regression methods. The results show that while the two QML models gave comparable accuracy scores to some of the classical models, the best scores were still achieved by some of the more advanced classical algorithms such as CatBoost. Further analysis of the QNN and QLSTM algorithms using multiple ansätz designs showed that increased circuit complexity did not yield substantial improvements in prediction accuracy. These findings suggest that QML models, especially QLSTM with simpler ansätz designs, offer a promising approach for modeling high-energy physics data, and highlight the importance of balancing circuit complexity with performance. The study also underscores the need for further evaluation of these algorithms on quantum hardware to better understand real-world applicability.
Abstract Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning that aims to enhance classical learning methods by leveraging principles such as superposition and entanglement. However, skepticism remains regarding its practical advantages, particularly due to the limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns through a systematic evaluation of Quantum Neural Networks (QNNs), quantum-inspired counterparts of Artificial Neural Networks (ANNs). We construct and analyze 12 distinct QNN configurations by combining two quantum feature maps with six entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset show that QNNs can achieve competitive or superior performance compared to classical baseline models, with the best-performing configuration (QNN-3) reaching an R 2 score of up to 0.94 when forecasting wind power output using four input features. In addition, we provide a detailed analysis of simulation time across different configurations, highlighting trade-offs between model complexity and computational cost. These findings demonstrate the potential of QNNs as viable models for predictive tasks and contribute to the ongoing evaluation of QML in real-world applications.
Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning, aiming to enhance classical machine learning methods by leveraging quantum mechanics principles such as entanglement and superposition. However, skepticism persists regarding the practical advantages of QML, mainly due to the current limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns by extensively assessing Quantum Neural Networks (QNNs)-quantum-inspired counterparts of Artificial Neural Networks (ANNs), demonstrating their effectiveness compared to classical methods. We systematically construct and evaluate twelve distinct QNN configurations, utilizing two unique quantum feature maps combined with six different entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset reveal that QNNs employing the Z feature map achieve up to 93% prediction accuracy when forecasting wind power output using only four input parameters. Our findings show that QNNs outperform classical methods in predictive tasks, underscoring the potential of QML in real-world applications.
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.
Quantum Neural Networks (QNN) and Quantum Long Short-Term Memory (QLSTM) models are emerging as powerful tools in quantum machine learning. The effectiveness of these models is largely governed by the structure of their parameterized quantum circuits, also known as Ansätze. The Expressibility (measure of how well an ansätz can explore Hilbert space), Entanglement (which governs the correlation between states of multiple qubits), and Depth (a measure of how many layers of quantum gates a circuit has) are fundamental to a model’s capacity to learn complex patterns. In this paper, we analyze how varying these attributes for various ansätze influences classification cost and accuracy. We evaluated QNN and QLSTM models across three cybersecurity datasets using ten ansätze with varying expressibility and complexity. Results showed that ansätze with lower expressibility and complexity achieved accuracy and training stability comparable to the more complex ones. Additionally, we found that QNN models consistently offered a better trade-off between accuracy, training time, and computational efficiency compared to QLSTM models.
Quantum computing combined with machine learning (ML) is a highly promising research area, with numerous studies demonstrating that quantum machine learning (QML) is expected to solve scientific problems more effectively than classical ML. In this work, we present Quantum Long Short-Term Memory (QLSTM), a QML architecture, and demonstrate its effectiveness in drug discovery. We evaluate QLSTM on five benchmark datasets (BBBP, BACE, SIDER, BCAP37, T-47D), and observe consistent performance gains over classical LSTM, with ROC-AUC improvements ranging from 3% to over 6%. Furthermore, QLSTM exhibits improved predictive accuracy as the number of qubits increases, and faster convergence than classical LSTM under the same training conditions. Notably, QLSTM maintains strong robustness against quantum computer noise, outperforming noise-free classical LSTM in certain settings. These findings highlight the potential of QLSTM as a scalable and noise-resilient model for scientific applications, particularly as quantum hardware continues to advance in qubit capacity and fidelity.