As the dimensionality and complexity of engineering data grows, traditional machine learning systems become increasinglydifficult to apply, especially when detecting informative features without sacrificing predictive accuracy and computationalefficiency. In this paper we proposed a Hybrid Quantum-Classical Learning Framework for Intelligent Feature Optimization andPredictive Analytics in Engineering Applications. The framework is a combination of classical data preprocessing, intelligentfeature selection, quantum-enhanced feature representation, parameterized quantum circuits and predictive learning in a unifiedoptimization pipeline. Engineering features are first normalized and evaluated to find informative subsets and minimizeredundancy. These selected features are then mapped onto the quantum feature space and parameterized quantum circuits tocreate improved representations for predictive modeling. The optimization objective takes into account predictive loss, size ofthe feature subset and regularization to achieve an efficient feature configuration. This quantum-classical representation is fed to the classical predictive models for classification and evaluation of the models. The framework is assessed based on predictiveaccuracy, ROC properties, feature reduction, convergence behavior, computational time, scalability and prediction error. Thepotential of using intelligent feature optimization combined with quantum enhanced representation for engineering analytics isillustrated by comparing it to conventional machine learning and hybrid learning models. This study provides a flexibleframework that enables the merging of quantum and classic machine learning, and shows how hybrid architectures can be usedin high-dimensional engineering prediction.
As the dimensionality and complexity of engineering data grows, traditional machine learning systems become increasinglydifficult to apply, especially when detecting informative features without sacrificing predictive accuracy and computationalefficiency. In this paper we proposed a Hybrid Quantum-Classical Learning Framework for Intelligent Feature Optimization andPredictive Analytics in Engineering Applications. The framework is a combination of classical data preprocessing, intelligentfeature selection, quantum-enhanced feature representation, parameterized quantum circuits and predictive learning in a unifiedoptimization pipeline. Engineering features are first normalized and evaluated to find informative subsets and minimizeredundancy. These selected features are then mapped onto the quantum feature space and parameterized quantum circuits tocreate improved representations for predictive modeling. The optimization objective takes into account predictive loss, size ofthe feature subset and regularization to achieve an efficient feature configuration. This quantum-classical representation is fed to the classical predictive models for classification and evaluation of the models. The framework is assessed based on predictiveaccuracy, ROC properties, feature reduction, convergence behavior, computational time, scalability and prediction error. Thepotential of using intelligent feature optimization combined with quantum enhanced representation for engineering analytics isillustrated by comparing it to conventional machine learning and hybrid learning models. This study provides a flexibleframework that enables the merging of quantum and classic machine learning, and shows how hybrid architectures can be usedin high-dimensional engineering prediction.
High-dimensional classification demands expressive feature representations, robust optimization, and manageable computational cost. This paper proposes a hybrid quantum– classical framework combining classical feature screening and compression, a shallow parameterized quantum circuit, measurement-derived quantum features, and a regularized classical classifier. The design explicitly addresses the mismatch between hundreds of original attributes and the small qubit counts available to near-term devices. Its training objective combines cross-entropy, complexity regularization, and a noise-sensitivity penalty, while alternating classical and quantum parameter updates. A reproducible evaluation protocol is specified using stratified data partitions, classical and quantum baselines, noise sweeps, and resource accounting. To demonstrate how the analysis should be reported, illustrative numerical results and plots are provided; they are not outcomes of executed quantum experiments and must be replaced by measured values before publication. The framework offers a testable route for evaluating whether compact quantum representations improve predictive performance or robustness relative to equally budgeted classical models, without presupposing quantum advantage.
The rapid advancement of artificial intelligence (AI) and quantum computing has introduced new paradigms for solving computationally complex problems beyond the capabilities of classical systems. This paper presents a Quantum Intelligence Platform (QIP), a hybrid quantum-inspired computational framework designed to enhance intelligent decision prediction and optimization in complex environments. The proposed system integrates quantum probabilistic modelling concepts such as superposition and interference with classical machine learning algorithms to improve prediction accuracy and adaptive decision-making. A hybrid architecture combining quantum circuit simulation and classical deep learning modules is developed to enable scalable experimentation without requiring physical quantum hardware. The platform is evaluated on decision prediction and optimization tasks, demonstrating improvements in convergence stability and predictive performance compared to conventional models. Experimental results show that quantum-inspired state representation enhances feature expressiveness and probabilistic reasoning. The proposed framework bridges the gap between theoretical quantum computation and practical intelligent systems, offering a scalable pathway for future quantum-enhanced AI applications.
Abstract This research presents a comprehensive hybrid quantum classical machine learning framework designed to overcome the computational limitations of classical algorithms when processing high dimensional data. The study integrates parameterized quantum circuits within classical neural network architectures and demonstrates their application across optimization tasks and supervised learning problems. The framework was evaluated on combinatorial optimization benchmarks including Max Cut and Job Shop Scheduling problems alongside classification tasks using quantum annealing based feature selection. Results demonstrate that hybrid quantum classical models achieve faster convergence rates and improved accuracy in high dimensional spaces compared to classical counterparts with the quantum algorithm for Max Cut optimization outperforming complete solution searches on larger problem instances. The findings provide machine learning practitioners and quantum computing researchers with validated hybrid architectures for near term quantum device deployment.
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.
This work investigates a hybrid quantum-classical machine learning methodology that combines deep learning with quantum computing to improve predictive analytics. The method starts by changing basic input features into the best mixed forms using classical weight matrices and quantum state encoding. A unitary operator refines quantum states to enhance feature extraction and representation. Gradient-based alterations repeatedly modify feature weights during optimization to improve accuracy. Quantum circuits not only change data but also enhance system separability and resilience. Normalizing and enhancing extracted properties using quantum and classical transformations ensures stability and consistency. Performance trials demonstrate that the hybrid technique improves accuracy, precision, recall, computational complexity, execution time, and energy economy. The hybrid model always outperforms machine learning, deep learning, and quantum mechanics-based approaches. The technique is now more scalable, making enormous data sets easier to analyze. Faster convergence rates and noise tolerance improve model performance in real-world applications. The findings suggest that quantum advances and classical learning approaches improve model interpretability and decision-making reliability. The research found hybrid quantum-classical machine learning to be a powerful, scalable, and efficient predictive modeling solution. This method improves precision while conserving computing power. By using both paradigms, the technique provides deeper insights and better predictions. As a major facilitator for future AI-driven applications, the hybrid model provides a possible solution for difficult machine learning challenges. These developments make hybrid models available.
Quantum computing has emerged as one of the most influential paradigms capable of transforming computational processes that struggle under classical limitations. In recent years, hybrid quantum–classical algorithms have gained significant attention for their ability to leverage the strengths of both architectures, especially within the Noisy Intermediate-Scale Quantum (NISQ) era. This research paper explores a quantum-enhanced optimization framework designed to improve machine learning model performance through parameterized quantum circuits (PQCs). The goal of this study is to provide a practical, scalable approach that can be integrated into existing machine learning workflows without requiring fully developed fault-tolerant quantum hardware. The proposed framework uses classical preprocessing to encode data into quantum states, followed by quantum transformations that explore richer feature spaces due to quantum superposition and entanglement. A classical optimizer then updates the quantum circuit parameters based on measurement results, forming an end-to-end feedback loop. This hybrid model aims to demonstrate improved optimization efficiency, reduction in loss-function plateaus, and better generalization for specific ML tasks involving small to medium-sized datasets. This paper provides a comprehensive investigation of methodology, literature background, and experimental reasoning with statistical analysis, along with conceptual diagrams illustrating system architecture and circuit layouts. The outcomes further strengthen the viability of hybrid quantum–classical computation as a promising direction for advanced machine learning systems.