To address the security and resource challenges in traditional marine edge computing networks (MECNs), this article proposes a quantum-empowered marine edge computing network (QMN) architecture. QMN enhances the edge computing performance in marine scenarios through a multi-layered approach, including the underwater, surface, and aerial layers. Specifically, QMN employs quantum sensors across multiple layers in the marine network to improve environmental monitoring with superior sensitivity and accuracy. Then, quantum communication ensures the security of data transmission and task offloading against interception and eavesdropping attacks, while quantum computing accelerates the processing of complex maritime tasks and improves operational efficiency. Additionally, wireless quantum charging provides a sustainable energy solution to support continuous operation in challenging marine environments. Furthermore, by integrating four proposed novel quantum technologies into QMN, this architecture achieves superior monitoring, highly secure transmission, efficient task processing and energy supply capabilities for MECNs. A case study simulates the quantum task offloading and quantum edge computing within QMN. Simulation results demonstrate the improvement of security and computational performance achieved by QMN. Open research directions for quantum restrictions in QMN are also discussed.
To address the security and resource challenges in traditional marine edge computing networks (MECNs), this article proposes a quantum-empowered marine edge computing network (QMN) architecture. QMN enhances the edge computing performance in marine scenarios through a multi-layered approach, including the underwater, surface, and aerial layers. Specifically, QMN employs quantum sensors across multiple layers in the marine network to improve environmental monitoring with superior sensitivity and accuracy. Then, quantum communication ensures the security of data transmission and task offloading against interception and eavesdropping attacks, while quantum computing accelerates the processing of complex maritime tasks and improves operational efficiency. Additionally, wireless quantum charging provides a sustainable energy solution to support continuous operation in challenging marine environments. Furthermore, by integrating four proposed novel quantum technologies into QMN, this architecture achieves superior monitoring, highly secure transmission, efficient task processing and energy supply capabilities for MECNs. A case study simulates the quantum task offloading and quantum edge computing within QMN. Simulation results demonstrate the improvement of security and computational performance achieved by QMN. Open research directions for quantum restrictions in QMN are also discussed.
The objective of this research is to explore the integration of quantum computing with multi-cloud architectures, aiming to enhance computational efficiency and security in advanced cloud environments. The study seeks to identify the potential benefits and challenges of incorporating quantum computing capabilities within a multi-cloud framework and to evaluate the impact on performance and security metrics. The research employs a hybrid methodological approach, combining both theoretical analysis and practical implementation. Initially, a detailed literature review is conducted to understand the current state of quantum computing and multi-cloud architectures. This is followed by the design and development of an integration framework that leverages quantum computing technologies in a multi-cloud environment. Key steps include developing a multi-cloud architecture that integrates quantum computing resources alongside classical computing resources, deploying quantum algorithms and protocols within the multi-cloud setup, implementing advanced security measures to protect data and computational processes, using a set of predefined metrics to evaluate computational efficiency and security, and employing statistical tools and techniques to analyze the collected data and draw meaningful insights. The integration of quantum computing with multi-cloud architectures resulted in significant improvements in computational efficiency, particularly in tasks that are traditionally resource-intensive. Key findings include enhanced computational speed, where quantum algorithms demonstrated superior performance in solving complex problems compared to classical algorithms, optimized resource utilization through dynamic allocation of quantum and classical resources leading to cost efficiency, improved security with quantum-enhanced protocols providing robust protection against cyber threats, and high scalability of the integrated architecture to accommodate increasing computational demands without compromising performance. The research concludes that integrating quantum computing with multi-cloud architectures offers substantial benefits in terms of computational efficiency and security. The findings indicate that such integration can revolutionize cloud computing, providing a powerful platform for handling complex computations and enhancing data security. However, the study also highlights several challenges, including the need for specialized hardware, the complexity of integration, and the necessity for ongoing research to fully harness the potential of quantum computing in cloud environments. Future research should focus on addressing these challenges and exploring further applications of quantum computing in various cloud-based scenarios.
Next-generation networks rely on edge computing for low-latency, distributed processing, yet face critical security vulnerabilities (e.g., eavesdropping) and fault tolerance challenges. To address these limitations, we propose a quantumedge computing framework integrating Quantum Key Distribution (QKD) with classical edge architecture enhanced by quantum teleportation and Einstein-Podolsky-Rosen (EPR) pair management for secure, distributed processing. QKD enables information-theoretically secure key exchange by leveraging quantum principles like no-cloning and entanglement, while quantum error correction enhances resilience against node failures. Our hybrid approach combines edge computing’s efficiency with quantum security, ensuring robust operation in adversarial environments. For the validation we have simulated hybrid network topology consisting of 6 different nodes acting as edge and user equipments. Data is being exchanged between nodes in the form of key pairs. To evaluate fault tolerance and security resilience, we simulated 3 scenarios: 1) Normal operating state, 2) Induce fault state and 3) Security breach state. The Simulations demonstrate significant improvements: 10× faster attack detection, 25× faster recovery, and 1.85× higher task success under simultaneous failure and attack. Overall, the quantum-edge framework achieves a 2.88× performance boost, offering a 3× advantage over classical systems.
The integration of quantum communication principles with 5G technology presents a groundbreaking approach to addressing the growing demand for high-speed, secure, and resilient communication systems. This paper proposes a Quantum-Driven Hybrid Network Architecture that leverages Quantum Key Distribution (QKD) for unparalleled security and combines it with 5G's ultra-fast data transmission capabilities. The architecture integrates key components such as QKD modules, 5G infrastructure, and edge computing, organized into a layered framework comprising quantum, network control, and application layers. Simulation results demonstrate significant improvements in throughput (up to 23.5%), latency reduction (up to 25%), and error rate reduction in high-noise environments (from 0.25% to 0.05%). The proposed system also exhibits enhanced security, achieving a 100% success rate in mitigating eavesdropping and man-in-the-middle attacks. Using resource-efficient mechanisms, the hybrid network achieves an overall accuracy of 97.8% in secure and resilient data transmission. This work validates the potential of quantum-enhanced 5G systems to meet the challenges of future communication networks.
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.
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.