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Computers, materials & continua/Computers, materials & continua (Print)

Quantum-Enhanced Edge Offloading and Resource Scheduling with Privacy-Preserving Machine Learning

Junjie Cao, Zhiyong Yu, Xiaotao Xu, Baohong Zhu, Jian Yang

Published Jan 1, 2025• 5 citations
Publisher

Abstract

This paper introduces a quantum-enhanced edge computing framework that synergizes quantum-inspired algorithms with advanced machine learning techniques to optimize real-time task offloading in edge computing environments. Thi... | Find, read and cite all the research you need on Tech Science Press
DOI
10.32604/cmc.2025.062371

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Quantum Machine Learning

Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security. In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future. Readers will find the volume: Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions; Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things; Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments; Comprehensively covers a wide range of topics. Audience Researchers and engineers in machine learning, quantum computing, data science, the Internet of Things.

Wiley eBooks•May 22, 2026•0 citations
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.
Revolutionize your IoT infrastructure with this guide to mastering quantum-enhanced machine learning algorithms and theoretical frameworks that are shattering the boundaries of classical computing to deliver unprecedented network performance and security. In a world increasingly reliant on interconnected devices and data-driven insights, the limitations of classical computing become ever more apparent. The convergence of quantum computing, machine learning, and the Internet of Things (IoT) heralds a new era of technological advancement, one where the boundaries of computational possibility are continually redefined. This book offers an in-depth examination of how quantum algorithms are utilized to improve the performance, security, and efficiency of IoT devices and networks. It connects theoretical concepts with practical applications, providing a comprehensive look at fundamental principles and advanced techniques in this rapidly growing field. Using case studies and real-world insights, this book gives readers the latest developments in quantum machine learning, artificial intelligence, and the smart Internet of Things, and their potential to create an accessible pathway to the future. Readers will find the volume: Demonstrates how to seamlessly integrate quantum computing and machine learning for next-gen IoT solutions; Explores the emerging field of quantum machine learning and its various applications for the AI-driven Internet of Things; Provides real-world examples and case studies demonstrating the power of quantum machine learning in smart IoT environments; Comprehensively covers a wide range of topics. Audience Researchers and engineers in machine learning, quantum computing, data science, the Internet of Things.