Abstract The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum‐secured smart lock etc.
Abstract The rapidly developing discipline of quantum computing (QC) employs ideas from quantum physics to improve the performance of traditional computers and other devices. Because of the dramatically improved speed at which it processes data, it can be applied to various issues. QC has many potential applications, but three of the most exciting applications are unstructured search, quantum simulation, and network optimisation. Several existing technologies, such as machine learning, may benefit from its increased speed and precision. In this study, the authors will explore how the principles of QC might be applied to the Internet of Things (IoT) to improve its accuracy, speed, and security. Several approaches exist for achieving this goal, such as network optimisation in IoT using QC, faster computation at IoT endpoints, securing IoT using QC, a quantum sensor for IoT, quantum digital marketing, quantum‐secured smart lock etc.
The rapid expansion of the Internet of Things (IoT) has transformed the way physical devices communicate, process information, and provide intelligent services.With billions of interconnected devices generating enormous volumes of heterogeneous data, conventional computing architectures increasingly face limitations in computational efficiency, security, optimisation, and scalability.Quantum computing has emerged as a disruptive paradigm capable of solving computationally intensive problems exponentially faster than classical computers for selected problem classes.Integrating quantum computing with IoT offers unprecedented opportunities for intelligent decision-making, real-time optimisation, secure communication, and enhanced machine learning.Quantum-enhanced IoT (QIoT) combines quantum algorithms, quantum machine learning, quantum communication, and post-quantum cryptography to create highly efficient and secure smart systems.Applications span healthcare, industrial automation, smart cities,
This chapter explores the transformative integration of Quantum Computing (QC) with the Internet of Things (IoT), highlighting significant advancements in security, optimization, and intelligent decision-making for cyber-physical systems. It provides a detailed review of quantum-enhanced algorithms, architectures, and sensing technologies designed for IoT ecosystems. Addressing the limitations of traditional methods in large-scale computation, network optimization, data analytics, and security, the chapter emphasizes core quantum concepts, i.e., superposition, entanglement, and interference. Techniques, including quantum annealing and the QAOA, enable efficient resource allocation, scheduling, and routing in emerging infrastructures like 6G and UAV networks. The use of hybrid quantum–classical models is examined for fault detection, cryptographic key generation, and real-time analytics across edge-to-cloud environments. QML has been shown to enhance adaptive learning and pattern recognition on resource-limited devices. To counter emerging cyber threats, quantum-resilient security solutions, including post-quantum cryptography, quantum random number generation, and quantum key distribution, are discussed. The chapter also highlights quantum sensors that utilize tunnelling, spin dynamics, and interference for applications in biomedical diagnostics, environmental monitoring, infrastructure evaluation, and autonomous navigation. Emerging use cases, including quantum-powered digital marketing and quantum-secured smart locks, are introduced. The chapter concludes by underscoring the transformative role of scalable hybrid QC–IoT architectures, which are poised to advance edge intelligence, smart infrastructure, healthcare, and logistics, while opening new directions for research and real-world deployment.
The Internet of Things (IoT) has emerged as an innovative framework, connecting physical devices into intricate networks that monitor and control various aspects of daily life. While traditional technologies such as big data analytics and cloud computing have driven the growth of IoT, the emergence of quantum technologies, spanning quantum computing, quantum communication, and quantum sensing, presents unprecedented opportunities to enhance IoT systems. This paper explores the realistic potential and challenges of integrating quantum technologies into IoT, emphasizing the need for a nuanced understanding of what quantum technologies can achieve to bridge the gap between promise and practical application. By fostering collaboration between disciplines, quantum technologies can shape the future of IoT, improving its security, efficiency, and precision.
Quantum computing represents the quantum mechanical concept, may have important benefits over the traditional computing. This benefit of quantum computing offers solutions to numerous challenges in secure communication and banking that were previously intractable. The Internet of Things (IoT) is a new technology which works with a large amount of data. Securing IoT data communication is necessary. Cryptographic algorithms like RSA and ECC are the foundation of the IoT security architecture that is now in place. These algorithms’ security was significantly impacted by quantum computing. As a result, our research examines the security issues with quantum-based technologies and IoT smart applications. This article offers an overview of the foundations of quantum computing and how it affects the security of IoT devices. This work intends to present a comprehensive view of IoT communication facilitated by quantum technology. In our work, we also analyse the main obstacles to deploying quantum-enabled communication.
The Internet of Things (IoT) has rapidly transformed industries by enabling the interconnectivity of devices and the real-time exchange of data. However, as IoT networks continue to scale, the limitations of traditional edge and cloud computing architectures—such as latency, security concerns, and scalability challenges—become more pronounced. This article explores the emerging role of quantum computing in addressing these challenges and advancing the evolution of edge and cloud solutions for IoT systems. By leveraging the unique capabilities of quantum computing, including superposition, entanglement, and quantum parallelism, IoT systems can achieve enhanced data processing speeds, improved security, and more efficient resource management. This article discusses the potential benefits of integrating quantum computing with edge and cloud infrastructures, focusing on use cases in real-time analytics, secure communication, and advanced machine learning for IoT applications. Additionally, it examines the technical, ethical, and regulatory challenges that need to be addressed for the successful deployment of quantum-edge cloud solutions. Finally, the article highlights the importance of collaboration between academia, industry, and policymakers to drive the development of quantum-enabled IoT systems. The findings suggest that quantum computing will play a pivotal role in the next generation of IoT infrastructure, unlocking new possibilities for smarter, more responsive, and secure IoT ecosystems.
The Internet of Things (IoT) is a constantly expanding system connecting countless devices for seamless data collection and exchange. This has transformed decision-making with data-driven insights across different domains. However, challenges arise concerning security and computational limitations. To strengthen IoT against cyber threats and optimize resource usage, combining quantum computing (QC) with machine learning (ML) is a promising approach. ML enables computers to learn from data and detect patterns without explicit programming. By leveraging ML algorithms, vast datasets from IoT devices can be analyzed, identifying anomalies and forecasting potential security breaches. Yet, conventional ML algorithms may need help with the complexity and scale of IoT data. QC, based on quantum mechanics, offers unparalleled computational speed and scale. Quantum ML algorithms can quickly analyze IoT datasets, identifying patterns and potential threats. This study examines the ideas behind ML, QC, and their potential collaboration within IoT networks. The research focuses on the possibility of improving the security of IoT networks by integrating QC approaches with ML. It also addresses the challenges and limitations of integrating ML and QC in the context of IoT networks. These obstacles include hardware constraints, algorithm complexities, and the need for specialized knowledge.