Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
Sensors, enabling observations across vast spatial, spectral, and temporal scales, are major data generators for information technology (IT). Processing, storing, and communicating this ever-growing amount of data pose challenges for the current IT infrastructure. Edge computing—an emerging paradigm to overcome the shortcomings of cloud-based computing—could address these challenges. Furthermore, emerging technologies such as quantum computing, quantum sensing, and quantum communications have the potential to fill the performance gaps left by their classical counterparts. Here, we present the concept of an edge quantum computing (EQC) simulator—a platform for designing the next generation of edge computing applications. An EQC simulator is envisioned to integrate elements from both quantum technologies and edge computing to allow studies of quantum edge applications. The presented concept is motivated by the increasing demand for more sensitive and precise sensors that can operate faster at lower power consumption, generating both larger and denser datasets. These demands may be fulfilled with edge quantum sensor networks. Envisioning the EQC era, we present our view on how such a scenario may be amenable to quantification and design. Given the cost and complexity of quantum systems, constructing physical prototypes to explore design and optimization spaces is not sustainable, necessitating EQC infrastructure and component simulators to aid in co-design. We discuss what such a simulator may entail and possible use cases that invoke quantum computing at the edge integrated with new sensor infrastructures.
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.
In modern power systems, edge devices serve as local hubs that collect data, perform on-site computing, sense electrical parameters, execute control actions, and communicate with neighboring edge devices as part of the larger grid. However, as the number of monitored nodes and control loops grows, traditional edge devices face serious limits. They can become overloaded by complex signal processing and decision tasks, causing delays and higher energy use. Standard sensors hit a noise floor that prevents them from detecting miniature changes, making it harder to spot early signs of faults or instability. Meanwhile, conventional communication links struggle with bandwidth limits, security risks, and rising encryption demands, which together slow down and weaken the transfer of critical grid information. Quantum technologies have the potential to overcome these challenges. Quantum computers can deliver exponential speed-ups for optimization and machine-learning tasks that ordinary processors cannot handle. Quantum sensors can sense signals with atomic precision, giving edge devices a more precise view of grid dynamics. Quantum communication techniques, including quantum key distribution, offer methods to achieve information-theoretic security and ensure that information arrives quickly and without tampering. We explore how quantum technologies can be integrated into edge devices, highlighting both opportunities and challenges.
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 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.
Quantum technology is revolutionizing the next generation of intelligent and secure sensing systems.This review explores how quantum sensors—leveraging principles of superposition, entanglement, and quantum tunneling—can be integrated with Internet of Things (IoT) and cloud computing infrastructures to achieve ultra-high precision, real-time analytics, and quantum-secure communication.It discusses the fundamental mechanisms of quantum sensing, sensor architectures, and hybrid quantum-classical data models.The paper also reviews Quantum-Enabled IoT (QIoT) frameworks, Quantum Cloud Computing (QCC) platforms such as IBM Quantum and AWS Braket, and highlights challenges including decoherence, scalability, and standardization.Finally, it presents the future direction of Quantum Industry 5.0, emphasizing sustainable, secure, and intelligent cyber-physical systems powered by quantum innovation.
Edge computing network and quantum network are two emerging technologies in current communication fields. Edge computing has emerged to support the computational demand of delay-sensitive applications in which substantial computing and storage are deployed at the network edge close to data sources. Quantum network supports distributed quantum computing, which could provide exponentially computation capabilities for certain problems. The vision of a hybrid quantum-edge is to provide a fundamentally new computing paradigm by expanding the computing capabilities and security of edge computing with quantum computing and quantum communications. The distributed nature of edge computing networks will also enable new scalable quantum networking schemes and applications. Such a hybrid computing paradigm will achieve unparalleled capabilities that are provably impossible by using only classical computing or quantum computing schemes alone. In this paper, we introduce the concept of hybrid quantum-edge computing network and discuss its challenges and opportunities.