The convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edge devices poses challenges due to limited resources such as power, memory, and computational capability. Quantum-inspired optimization algorithms (QIOAs) offer a promising alternative to traditional optimization techniques, providing superior performance in constrained environments. This paper explores the application of QIOAs for scalable machine learning models in edge computing. We propose a novel hybrid quantum-inspired approach, benchmark it against classical algorithms, and demonstrate its efficacy in various edge computing environments. Experimental results show improved accuracy, reduced latency, and enhanced resource utilization.
The convergence of machine learning and edge computing has led to the development of scalable solutions that bring computation closer to the data source. However, optimizing machine learning models efficiently for edge devices poses challenges due to limited resources such as power, memory, and computational capability. Quantum-inspired optimization algorithms (QIOAs) offer a promising alternative to traditional optimization techniques, providing superior performance in constrained environments. This paper explores the application of QIOAs for scalable machine learning models in edge computing. We propose a novel hybrid quantum-inspired approach, benchmark it against classical algorithms, and demonstrate its efficacy in various edge computing environments. Experimental results show improved accuracy, reduced latency, and enhanced resource utilization.
Quantum Machine Learning (QML) represents a transformative intersection of quantum computing and artificial intelligence, promising exponential speed-ups and new capabilities for data-driven tasks. However, the practical realization of QML is hindered by the current limitations of quantum hardware, including scalability constraints, qubit decoherence, and high error rates. Hybrid quantum-classical architectures have emerged as a pragmatic approach, leveraging classical resources alongside quantum processors to mitigate these hardware challenges. A promising direction for advancing QML is the integration of hybrid architectures into cloud computing platforms and mobile-edge environments. This approach enables scalable access to quantum resources, facilitates real-time data processing, and brings quantum-enhanced intelligence closer to end-users and devices. This article reviews the state-of-the-art in QML, focusing on how hybrid models and quantum algorithms are addressing the dual challenges of hardware scalability and architectural integration within cloud and mobile-edge computing contexts. Recent advances are analyzed, benchmark results are highlighted, and future directions for deploying robust, scalable QML systems in distributed computing environments are discussed.
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 training of large-scale machine learning models presents significant computational challenges, primarily due to the inherent complexity of optimization algorithms like stochastic gradient descent (SGD) and its variants. These algorithms often struggle with convergence speed, getting trapped in local minima, and the ability to effectively explore the vast solution space. This paper investigates the potential of quantum-inspired optimization techniques to address these limitations. Specifically, we explore the application of quantum annealing and variational quantum eigensolver (VQE) approaches to accelerate the training and optimization process of large-scale machine learning models. Our core claim is that by mapping the optimization problem to a quantum system, we can leverage quantum computation to overcome the computational bottlenecks associated with classical optimization methods. We propose a framework where the objective function of the machine learning model is translated into a Hamiltonian for a quantum annealer or the cost function for a VQE, enabling a quantum-inspired solution. The results, while theoretical in this initial exploration, demonstrate a potential pathway for significantly reducing training times and improving the overall performance of large-scale machine learning models. Further research is needed to develop efficient hybrid quantum-classical algorithms and explore the practical limitations of this approach.
Quantum Machine Learning (QML) is an emerging interdisciplinary field that combines the computational power of quantum computing with machine learning techniques to address the scalability challenges faced by classical AI systems. As datasets and models grow in complexity, traditional machine learning algorithms encounter limitations related to processing speed, accuracy, and resource efficiency. This research explores the potential of QML to overcome these barriers by leveraging quantum algorithms such as Quantum Approximate Optimization Algorithms (QAOA), Quantum Support Vector Machines (QSVM), and Quantum Neural Networks (QNNs) to enhance the scalability and efficiency of AI applications. Through a combination of literature review, algorithm simulation, and hybrid quantum-classical approaches, the study evaluates the speedup, accuracy improvements, and resource efficiency that quantum-enhanced models can offer. The research also investigates practical applications of QML in domains such as healthcare, finance, and climate science. Despite significant advancements, challenges related to quantum hardware, error correction, and noise remain, which will be discussed alongside strategies for overcoming these issues. The findings suggest that while quantum machine learning holds immense potential, its full impact on scalable AI will require further advancements in quantum computing and its integration with classical systems.
The integration of Quantum-Inspired Reinforcement Learning (QiRL) into Edge-Driven CyberPhysical Systems (CPS) presents a transformative approach to enhance real-time decision-making in distributed environments. While classical reinforcement learning (RL) struggles with scalability and computational complexity in edge-CPS, quantum-inspired Quantum-Inspired Reinforcement Learning for Edge-Driven Cyber-Physical Systems algorithms leverage superposition and entanglement principles to optimize policy exploration and convergence. This paper proposes a novel QiRL framework that combines quantum-inspired optimization with deep RL, enabling efficient resource allocation and adaptive control in latency-sensitive applications (e.g., smart grids, autonomous drones). We formalize the mathematical foundations of QiRL, design a hybrid neural-quantum architecture for edge deployment, and evaluate its performance under dynamic CPS constraints. Simulations on IoT-based case studies demonstrate 20−35% faster convergence and 15% higher energy efficiency compared to classical Deep Q-Networks (DQN). Key challenges, such as quantum noise simulation and edge-hardware limitations, are discussed to guide future research. This work bridges quantum computing principles with edge intelligence, offering a scalable pathway for nextgeneration CPS.
The rapid growth of data generated by Internet of Things (IoT) devices necessitates the development of advanced computational frameworks that can efficiently handle real-time data processing. Traditional cloud and edge computing systems, while effective, face significant challenges, including high latency, limited computational resources, and data transfer bottlenecks. To address these issues, this study introduces a hybrid approach that integrates quantum computing into the edge-cloud continuum. The proposed method utilizes Quantum- Neural Networks (QNNs) in conjunction with local edge processing, leveraging quantum autoencoders for feature compression, and incorporating fully decentralized edge computing with quantum processors to enhance data processing capabilities. Additionally, a hybrid model combining QNNs with classical machine learning techniques is implemented at the edge for improved classification accuracy. The proposed system's performance is evaluated through extensive experiments across multiple datasets, comparing the results of quantum-based models with traditional classical and hybrid approaches. The results show a significant improvement in classification accuracy, reduced feature compression time, and enhanced processing efficiency, particularly in resource-constrained environments. The results underscore the potential of quantum machine learning in real-time, large-scale applications, marking an important step toward the practical integration of quantum computing in distributed computing systems.