Abstract The amount and difficulty of data have both grown a lot since the number of Internet of Things (IoT) devices in the medical and industrial sectors has grown a lot. These situations with a lot of data have needs that go beyond what most cloud and edge computing frameworks can do when it comes to processing in real time, energy efficiency, and security. The inclusion of quantum computing, which can handle a lot of processing at once and execute more sophisticated tasks at once, could transform the way things are done in a big manner. When it comes to handling sensitive and different IoT, the current Edge-Cloud designs have a few big problems: they don't scale effectively, they have a lot of latency, and they do not have enough capacity. In order for predictive maintenance and automation to perform well in industrial Internet of Things (IIoT) contexts, they need to be able to address problems and act promptly. On the other hand, late diagnoses and data breaches are very dangerous for medical settings. All of these key factors; the computing burden, the reaction speed, and the security are handled by one unified design. The Quantum-Enhanced Edge-Cloud Computing Framework (QEECF) is proposed to speed up essential operations including encryption, finding anomalies, and making decisions in real time. With this technology, the quantum processing units (QPUs) are added to both the cloud servers and the edge nodes at the same time. The framework uses a hybrid quantum-classical scheduling strategy that combines quantum annealing with variational circuits to give the best possible job offloading. The objective of a federated quantum learning system is to keep sensitive data safe. This data is stored in databases all around the world, like those used in business and healthcare. We evaluate the technology by running simulations of things like smart manufacturing and keeping an eye on healthcare. These simulations are easier using IBM Qiskit and edge simulators. When compared to standard Edge-Cloud arrangements, QEECF might increase data throughput by 36%, cut job delays by 43%, and improve the accuracy of finding anomalies by 48%.
Abstract The amount and difficulty of data have both grown a lot since the number of Internet of Things (IoT) devices in the medical and industrial sectors has grown a lot. These situations with a lot of data have needs that go beyond what most cloud and edge computing frameworks can do when it comes to processing in real time, energy efficiency, and security. The inclusion of quantum computing, which can handle a lot of processing at once and execute more sophisticated tasks at once, could transform the way things are done in a big manner. When it comes to handling sensitive and different IoT, the current Edge-Cloud designs have a few big problems: they don't scale effectively, they have a lot of latency, and they do not have enough capacity. In order for predictive maintenance and automation to perform well in industrial Internet of Things (IIoT) contexts, they need to be able to address problems and act promptly. On the other hand, late diagnoses and data breaches are very dangerous for medical settings. All of these key factors; the computing burden, the reaction speed, and the security are handled by one unified design. The Quantum-Enhanced Edge-Cloud Computing Framework (QEECF) is proposed to speed up essential operations including encryption, finding anomalies, and making decisions in real time. With this technology, the quantum processing units (QPUs) are added to both the cloud servers and the edge nodes at the same time. The framework uses a hybrid quantum-classical scheduling strategy that combines quantum annealing with variational circuits to give the best possible job offloading. The objective of a federated quantum learning system is to keep sensitive data safe. This data is stored in databases all around the world, like those used in business and healthcare. We evaluate the technology by running simulations of things like smart manufacturing and keeping an eye on healthcare. These simulations are easier using IBM Qiskit and edge simulators. When compared to standard Edge-Cloud arrangements, QEECF might increase data throughput by 36%, cut job delays by 43%, and improve the accuracy of finding anomalies by 48%.
The Internet of Things (IoT) is expanding rapidly, which has created a need for sophisticated computational frameworks that can handle the data and security requirements inherent in modern IoT applications. However, traditional cloud computing frameworks have struggled with latency, scalability, and security vulnerabilities. Quantum-Edge Cloud Computing (QECC) is a new paradigm that effectively addresses these challenges by combining the computational power of quantum computing, the low-latency benefits of edge computing, and the scalable resources of cloud computing. This study has been conducted based on a published literature review, performance improvements, and metrics data from Bangladesh on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. We have discussed the integration of quantum cryptography to enhance data integrity, the role of edge computing in reducing response times, and how cloud computing's resource abundance can support large IoT networks. We examine case studies, such as the use of quantum sensors in self-driving vehicles, to illustrate the real-world impact of QECC. Furthermore, the paper identifies future research directions, including developing quantum-resistant encryption and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC promises to overcome the existing limitations of IoT frameworks and set a new standard for the future of IoT applications.
The rapid expansion of the Internet of Things (IoT) has driven the need for advanced computational frameworks capable of handling the complex data processing and security challenges that modern IoT applications demand. However, traditional cloud computing frameworks face significant latency, scalability, and security issues. Quantum-Edge Cloud Computing (QECC) offers an innovative solution by integrating the computational power of quantum computing with the low-latency advantages of edge computing and the scalability of cloud computing resources. This study is grounded in an extensive literature review, performance improvements, and metrics data from Bangladesh, focusing on smart city infrastructure, healthcare monitoring, and the industrial IoT sector. The discussion covers vital elements, including integrating quantum cryptography to enhance data security, the critical role of edge computing in reducing response times, and cloud computing's ability to support large-scale IoT networks with its extensive resources. Through case studies such as the application of quantum sensors in autonomous vehicles, the practical impact of QECC is demonstrated. Additionally, the paper outlines future research opportunities, including developing quantum-resistant encryption techniques and optimizing quantum algorithms for edge computing. The convergence of these technologies in QECC has the potential to overcome the current limitations of IoT frameworks, setting a new standard for future IoT applications.
Nowadays, the industrial Internet of things (IIoT) and smart factories are relying on intelligence and big data analytics for large-scale decision making. Yet, this method is facing critical challenges regarding computation and data processing due to the complexity and heterogeneous nature of big data. Smart factory systems rely primarily on the analysis results to optimize production, predict future market directions, prevent and manage risks, and so on. However, deploying the existing classical solutions such as machine learning, cloud, and AI is not effective anymore. Smart factory systems and industries need novel solutions to sustain their development. On the other hand, with the fast development of quantum information systems (QISs), multiple sectors are studying the opportunities and challenges of implementing quantum-based solutions for a more efficient and exponentially faster processing time. To this end, in this paper, we discuss the implementation of quantum solutions for reliable and sustainable IIoT-based smart factory development. We depict various applications where quantum algorithms could improve the scalability and productivity of IIoT systems. Moreover, we design a universal system model where smart factories would not need to acquire quantum computers to run quantum algorithms based on their needs; instead, they can use quantum cloud servers and quantum terminals implemented at the edge layer to help them run the desired quantum algorithms without the need of an expert. To prove the feasibility of our model, we implement two real-world case studies and evaluate their performance. The analysis shows the benefits of quantum solutions in different sectors of smart factories.
Abstract Task offloading frameworks play a crucial role in modern healthcare by optimizing resource utilization, reducing computational burdens, and enabling real-time medical decision-making. However, existing Digital Twin (DT)-based healthcare models suffer from high latency, inefficient resource allocation, cybersecurity vulnerabilities, and computational limitations when processing large-scale patient data. These constraints pose significant risks in time-sensitive applications such as ICU monitoring, robotic-assisted surgeries, and telemedicine. To address these limitations, this paper introduces a Quantum-Enhanced DT-IoT framework, integrating Artificial Intelligence (AI), Quantum Computing (QC), DT, and the Internet of Things (IoT) for real-time, secure, and efficient healthcare task offloading. The proposed system introduces two key optimization algorithms: (1) DTH-ATB-MAPPO, which dynamically adjusts task scheduling and resource distribution, and (2) AQDT-IoT, which enhances computational efficiency and cybersecurity compliance in 6 G-enabled IoT networks. By leveraging Approximate Amplitude Encoding (AAE) and Grover’s search, the framework enhances task offloading efficiency, enabling faster decision-making and optimized resource distribution across 6 G-enabled IoT networks. Empirical evaluations show that quantum preprocessing improved Task Offloading Success Rate (TOSR) by 32% and reduced the Error Rate (ER) by 80%, significantly outperforming traditional DT-based healthcare models. These enhancements enable. Additionally, theoretical analysis demonstrates computational speed enhancements, adaptive cybersecurity mechanisms, and improved system scalability, positioning this framework as a viable candidate for future cloud-based quantum healthcare infrastructures, even in resource-constrained hospital environments.
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 blistering development of the Internet of Things (IoT) has brought an urgent necessity to an efficient data processing at the end of the network, where devices are characterized by few computational capabilities, and little to no energy availability. Conventional machine learning (ML) solutions can offer high-performance on a centralized system, but do not provide the edge environments with the required latency and the corresponding performance. This paper gives a quantum machine learning (QML)-backed architecture that is used to optimize IoT edge processing, inspiring quantum feature encoding along with quantum neural networks with classical post-processing resource allocation. Different simulated IoT datasets were tested on the framework and they all represent applications to smart home, industrial IoT, and environment monitoring. According to experimental findings, processing latency (improved by 39 percent), energy consumption (improved by 40 percent), memory reorganization (improved by 40 percent), and prediction accuracy (improved by 6 per cent.) were significantly less than conventional ML models. The results demonstrate that QML models are able to make real-time decisions significant and scalable as well as sustainable in edge computing. Quantum-enhanced edge intelligence can significantly improve IoT data processing speed, efficiency, and smart decision-making.