The integration of the Internet of Things (IoT) with quantum communication networks opens the door to transformative applications, including massive machine communication, virtual reality (VR), the Metaverse, and a wide range of artificial intelligence (AI)-driven technologies. The Quantum IoT (QIoT) meets these demands by enabling ultrasecure communication, advanced data processing, and enhanced sensing capabilities. By leveraging quantum principles, such as entanglement and teleportation, QIoT achieves superior efficiency and security compared to traditional IoT systems. As a pioneering quantum computing and communication convergence, QIoT is expected to experience significant growth over the next decade, driven by rapid advancements in quantum technologies. However, QIoT networks face critical challenges, such as energy efficiency (EE) issues, memory limitations caused by qubit decoherence, and substantial losses during quantum communication, including the exponential decay of entanglement success over quantum optical channels. To address these challenges, we propose an optimal decoherence-aware entanglement generation framework to maximize EE in QIoT networks. Using Dinkelbach’s method and Lagrangian analysis, the framework reformulates the resource allocation (RA) problem as a nonlinear fractional programming model, which is solved iteratively through linear programming techniques. This approach ensures energy-efficient entanglement generation rates (EGRs) while maintaining reliable quantum communication. Simulation results validate the effectiveness of the proposed framework, highlighting its advantages and providing valuable insights for the development of QIoT systems.
The integration of the Internet of Things (IoT) with quantum communication networks opens the door to transformative applications, including massive machine communication, virtual reality (VR), the Metaverse, and a wide range of artificial intelligence (AI)-driven technologies. The Quantum IoT (QIoT) meets these demands by enabling ultrasecure communication, advanced data processing, and enhanced sensing capabilities. By leveraging quantum principles, such as entanglement and teleportation, QIoT achieves superior efficiency and security compared to traditional IoT systems. As a pioneering quantum computing and communication convergence, QIoT is expected to experience significant growth over the next decade, driven by rapid advancements in quantum technologies. However, QIoT networks face critical challenges, such as energy efficiency (EE) issues, memory limitations caused by qubit decoherence, and substantial losses during quantum communication, including the exponential decay of entanglement success over quantum optical channels. To address these challenges, we propose an optimal decoherence-aware entanglement generation framework to maximize EE in QIoT networks. Using Dinkelbach’s method and Lagrangian analysis, the framework reformulates the resource allocation (RA) problem as a nonlinear fractional programming model, which is solved iteratively through linear programming techniques. This approach ensures energy-efficient entanglement generation rates (EGRs) while maintaining reliable quantum communication. Simulation results validate the effectiveness of the proposed framework, highlighting its advantages and providing valuable insights for the development of QIoT systems.
The relationship between quantum computing and the Internet of Things (IoT) is becoming a powerful pathway to develop secure, intelligent and sustainable network systems – however, present-day IoT architectures experience scalability challenges, as well as energy and data vulnerability, due to classical models in cryptography. This paper presents a Next Generation Quantum-IoT Communication Framework (QIoT-CF) that addresses these issues with quantum-based encryption, entanglement-assisted routing, and dynamic sustainability optimization for communication in the framework. The architecture uses Quantum Key Distribution (QKD) to ensure quality data security that is unbreakable, and it uses reinforcement learning–based queuing to balance energy consumption and communication reliability with heterogeneous IoT nodes. The simulations suggest that latency decreased by 45%, energy consumption improved by 43%, and the quantum bit error rate (QBER) improved by 52% relative to current IoT systems. The sustainability controller will change transmission policies based on carbon-aware energy metrics providing an acceptable trade-off between quantum performance and sustainability at-scale. The framework represents a critically significant advancement toward eco-intelligent quantum communication security situated within the 6G vision and beyond.
The fast growth of Internet of Things (IoT) networks has heightened the demand for secure, scalable and energy efficient communications methods. Traditional cryptographic techniques are more susceptible to quantum computing attacks and therefore have pushed the necessity for quantum secure methods. This study presents an energy efficient artificial intelligence assisted quantum secure communication framework designed to scale and provide reliable IoT networks. The framework uses Quantum Key Distribution (QKD) combined with deep learning optimization to improve both relationship and network performance. A self-adaptive AI module applies its lower level of computational resources and dynamically changing network conditions in order to optimize key management, routing decisions, and resource allocation, which reduces both energy consumption and communication latency. The proposed system is intended to support the specific types of constraints faced within an IoT environment. The experimental simulation results demonstrate that the framework improves energy efficiency, increases packet delivery ratio, and decreases latency in IVN (Intelligent Vehicular Networks) by comparison with traditional means of secure communication. In addition, by combining AI and quantum communications, we increase our resilience to threats posed by both classical and quantum forms of attack while providing an added layer of protection for transmitting data between devices in future wireless networks. Additionally, this proposed framework will provide a very scalable and effective method for providing IoT solutions within the environment of 5G mobile networks or later generations.
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,
The convergence of Sixth-Generation (6G) wireless networks and the Internet of Things (IoT) demands unprecedented levels of performance, scalability, and security. Traditional architectures are increasingly inadequate in addressing the computational and security challenges posed by massive IoT connectivity, ultra-low latency, and high data throughput. This paper proposes a novel quantum-assisted architecture that integrates quantum computing and quantum communication principles to enhance the efficiency and security of 6G-enabled IoT systems. The framework leverages quantum key distribution (QKD), quantum machine learning (QML), and entanglement-assisted routing to provide end-to-end encryption, intelligent resource allocation, and resilient data transmission. Our simulation results and comparative analysis demonstrate significant improvements in network throughput, latency, and security resilience compared to classical 6G-IoT architectures. This research establishes a foundational step toward realising secure and intelligent next-generation wireless networks through the integration of quantum technology.
Recent advancements in quantum computing have highlighted the necessity for specialized software to effectively address the unique challenges of quantum systems. The integration of quantum computing with IoT ecosystems, forming the Quantum Internet of Things (QIoT), offers transformative potential across security, efficiency, and computational scalability. This paper presents a review of QIoT architectures, focusing on their core dimensions, including security, blockchain integration, quantum-assisted machine learning, and quality attributes. By synthesizing findings and proposing a structured taxonomy, we address gaps and explore pathways for future research and innovation. The paper aims to serve as a roadmap for researchers and practitioners navigating the complexities of QIoT systems.
Quantum and Internet of Things (IoT) technologies are driving transformative advancements across various industries, and their integration into drone communications presents promising opportunities for enhancing drone capabilities. This abstract explores the potential of leveraging quantum communication principles in IoT-based drone systems to address key challenges, such as security, data processing, and long range connectivity. The conventional communication methods employed in drones often suffer from vulnerabilities, hindering the secure transmission of sensitive data. Quantum communication, with its inherent security features based on quantum key distribution and entanglement, offers a new paradigm for ensuring data integrity and confidentiality during drone operations. Furthermore, the exponential growth of IoT devices in drones generates massive amounts of data that require efficient processing and analysis. Quantum computing, with its immense computational power, has the potential to accelerate data processing in drone-based IoT applications, enabling real-time decision-making and resource optimization. Moreover, quantum-enhanced long-range communication can significantly extend the reach of drone networks, enabling seamless connectivity even in remote or challenging environments. Leveraging quantum entanglement for long-distance communication may enable efficient data exchange between drones and ground stations, leading to enhanced situational awareness and collaboration among drone fleets. Despite the promising prospects, several technical challenges need to be addressed in realizing quantum enabled IoT communications in drones. These include integrating quantum communication modules into drone platforms, developing robust quantum algorithms for IoT data processing, and addressing issues related to quantum channel noise and coherence