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Energy-Aware Benchmarking of Quantum Machine Learning for Financial Prediction Tasks: An Accuracy–Sustainability Pareto Analysis of Hybrid Quantum–Classical Pipelines

Galiveeti Poornima

Published Jan 1, 2026• 0 citations
Publisher
DOI
10.2139/ssrn.7650261

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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 forecasting of financial time series has gained more significance in decision making within a dynamic economic setting. Over the last few years, there has been a push in exploring both classical machine learning methods and novel quantum machine learning models with a view to enhance predictive accuracy. This paper suggests a quantum-classical regression framework developed that combines the feature selection in the form of a Random Forest in a hybrid approach, dimensionality reduction in the mode of Principal Component Analysis (PCA), and predicting in the form of a Variational Quantum Circuit (VQC). It is tested in various financial indicators, as BSE ESG, Carbon Exchange, Energy, Green Exchange, Oil and Gas and Power, with the help of RMSE and R 2 as the measure of performance. As experimental findings indicate, the classical algorithms like Random Forest and k-Nearest Neighbors always have the best accuracy and stability compared to quantum and hybrid ones. Quantum models are unstable, in contrast to Quantum kNN which is fairly stable. The hybrid model performs moderately with consistent error values but fairly lower explanatory power with significant results mostly on the BSE ESG data. In general, the paper has pointed out that classical approaches are still best in structured financial prediction tasks and hybrid quantum-classical models are potentially effective, but not yet competent. The results give valuable information on the existing limitation and future of the quantum machine learning to financial forecasting applications.
Accurate soil nutrient estimation underpins fertility assessment, precision agriculture, and sustainable land management, yet conventional laboratory analysis is slow, costly, and impractical at scale. Hyperspectral imaging (HSI) captures detailed spectral signatures linked to soil properties, but its high dimensionality and limited ground truth samples strain classical machine learning. This review examines quantum machine learning (QML) as an emerging rather than established direction for high-dimensional, low-sample hyperspectral soil analysis. We outline where classical machine learning and deep learning fall short and then assess how quantum kernel methods, variational quantum circuits, and hybrid quantum–classical architectures might improve feature representation and nonlinear modeling. Of the 150 studies reviewed, 26 report quantum machine learning results of any kind, five use soil data, and only one evaluates soil spectra directly for nutrient or property estimation. The remainder contribute transferable evidence from adjacent soil and remote sensing tasks, together with soil applications that remain proposals. Accordingly, the review weighs both the promise and the practical constraints of QML on current noisy intermediate-scale quantum hardware, including the encoding cost, measurement overhead, circuit depth, and trainability limits. Future research priorities are identified: quantum-ready hyperspectral soil datasets, reproducible benchmarking against well-tuned classical baselines, scalable hybrid pipelines, hardware-aware reporting, and field validation. Pairing QML with hyperspectral soil sensing may eventually support soil fertility assessment, variable-rate fertilization, and sustainable precision agriculture, but any such benefit must be demonstrated experimentally rather than inferred and will depend on continued progress in quantum hardware.