In recent years, distributed wireless communication optimization, where training data is stored remotely from local multi-access edge computing (MEC) processors to preserve data security privacy and minimize complexity, has seen noteworthy progress for relevant wireless Internet-of-Everything (WIoE) networks beyond 6G. Nonetheless, the exploding number of WIoE clients requires secure data storage and scaled data processing at the network and transmitter, which local processors might be unable to afford. Parallel to this, we are witnessing widespread quantum-enabled learning adoptions for optimizing wireless communications. The rapid growth of quantum technologies has introduced security concerns for classical channels, due to their potential to undermine classical cryptographic approaches. This paper, therefore, considers the adoption of quantum-enabled learning with quantum communication protocol, especially quantum secure direct communication (QSDC) via superdense coding. While the processing learning happens across different locations for next-generation WIoE networks, the QSDC prevents vulnerabilities of data poisoning and model stealing in connected quantum collaborative learning.
In recent years, distributed wireless communication optimization, where training data is stored remotely from local multi-access edge computing (MEC) processors to preserve data security privacy and minimize complexity, has seen noteworthy progress for relevant wireless Internet-of-Everything (WIoE) networks beyond 6G. Nonetheless, the exploding number of WIoE clients requires secure data storage and scaled data processing at the network and transmitter, which local processors might be unable to afford. Parallel to this, we are witnessing widespread quantum-enabled learning adoptions for optimizing wireless communications. The rapid growth of quantum technologies has introduced security concerns for classical channels, due to their potential to undermine classical cryptographic approaches. This paper, therefore, considers the adoption of quantum-enabled learning with quantum communication protocol, especially quantum secure direct communication (QSDC) via superdense coding. While the processing learning happens across different locations for next-generation WIoE networks, the QSDC prevents vulnerabilities of data poisoning and model stealing in connected quantum collaborative learning.
The Internet of Things (IoT) has revolutionized various sectors by enabling seamless device interaction. However, the proliferation of IoT devices has also raised significant security and privacy concerns. Traditional security measures often fail to address these concerns due to the unique characteristics of IoT networks, such as heterogeneity, scalability, and resource constraints. This survey paper adopts a thematic exploration approach for a comprehensive analysis to investigate the convergence of quantum computing, federated learning, and 6G wireless networks. This novel intersection is explored to significantly improve security and privacy within the IoT ecosystem. To enable several secure, intelligent IoT applications, quantum computing, with its superior computational capabilities, can strengthen encryption algorithms, making IoT data more secure. Federated learning, a decentralized machine learning approach, allows IoT devices to learn a shared model while keeping all the training data on the original device, thereby enhancing privacy. This synergy becomes even more crucial when integrated with the high-speed, low-latency capabilities of 6G networks, which can facilitate real-time, secure data processing and communication among many IoT devices. Second, we discuss the latest developments, offering an up-to-date overview of advanced solutions, available datasets, and key performance metrics and summarizing the vital insights, challenges, and trends in securing IoT systems. Third, we design a conceptual framework for integrating quantum computing in federated learning, adapted for 6G networks. Finally, we highlight the future advancements in quantum technologies and 6G networks and summarize the implications for IoT security, paving the way for researchers and practitioners in the field of IoT security.
Abstract Over the past four decades, quantum communication has evolved as a dynamic interdisciplinary field, advancing theoretical concepts and practical implementations. This article provides a concise overview focusing on recent progress in different aspects of secure quantum communication and quantum computation protocols, which can be applied to several real-world applications in quantum networks. These protocols guarantee unconditional security while enhancing communication rates and computation capabilities by harnessing quantum advantages. We also explore the role of non-terrestrial networks in quantum applications, with a focus on quantum technologies such as quantum key distribution and beyond, suitable for satellite-based applications. These technologies can contribute to future extensions of the quantum internet across intercontinental territories, connecting complex quantum network applications. Further, we delve into discussing the integration of quantum communication into 6G technology. The key innovation of this article lies in integrating quantum communication into 6G networks through a novel system-level simulation framework. 6G-enabled quantum networks are expected to meet the high demands on ubiquitous coverage, data rate, latency, and energy consumption. To address these issues, we design and evaluate four traffic demand scenarios using numerical simulation, illustrating how superdense coding doubles the data transmission rate and fulfills the high traffic demands on data rate, while under low traffic demand, entanglement resources can be reserved for future applications. Specifically, our investigation demonstrates how resource utilization adapts to different traffic demands, with adjustments based on available resources and practical constraints, evaluated over an ideal noise-free communication channel. The proof-of-work simulation is implemented using Python and is based on the system model we designed for varying traffic demands to pave the way for efficient quantum networks and gain deeper insights into their feasibility with available resources.
The rapid growth of the Internet of Things (IoT) is transforming industries such as healthcare, transportation, and smart infrastructure, but it also amplifies critical concerns regarding data security, privacy, and device authentication. Traditional cryptographic mechanisms offer partial protection, yet they remain vulnerable to emerging quantum computing threats, posing significant risks to IoT systems. Addressing these challenges, this paper introduces FLQC-IoT, a novel security framework that integrates federated learning (FL) with quantum computing (QC) to provide robust, scalable, and future-ready protection. Unlike conventional centralized approaches, FLQC-IoT enables decentralized model training directly on IoT devices, safeguarding sensitive data from exposure. Quantum technologies are incorporated at multiple layers: quantum neural networks accelerate optimization tasks, while quantum key distribution and quantum homomorphic encryption ensure tamper-proof key exchange and secure communication. Furthermore, the framework adapts dynamically to emerging threats through real-time anomaly detection and distributed intelligence, significantly enhancing resilience. Experimental results demonstrate a 15.32 % reduction in computational overhead and a 21.45 % decrease in authentication rounds compared to state-of-the-art methods, highlighting the framework’s efficiency and practical readiness. These findings underscore the significance and transformative impact of FLQC-IoT, providing a scalable, privacy-preserving, and resilient approach to securing IoT networks across critical domains, including healthcare, university campus networks, smart infrastructure, and transportation.
Quantum computing is on the cusp of transforming the computational strength of “Intelligent Transportation Systems” (ITSs), but issues such as the scalability of qubits and error correction continue. With wireless networks shifting from 5G to 6G, technologies such as extended mobile broadband (eMBB), ultra-reliable low-latency communications (URLLC), and the Internet of Things (IoT) will transform global connectivity. Though 5G offers improved speeds, it is prone to threats based on quantum computing, making quantum-safe cryptographic solutions for 6G networks essential. This paper discusses the revolutionary capability of quantum machine learning (QML) to meet the changing needs of future wireless systems. Through the use of quantum algorithms for data processing, QML can improve network performance, real-time decision-making, and increase security with quantum-safe cryptography. This paper sets out the challenges and prospects for the integration of quantum technologies into the design and deployment of secure, efficient, and scalable 6G wireless communication systems.
Machine learning models have scaled to unprecedented sizes, making training across distributed devices the de facto standard in the field. In this work, we explore how quantum communications can make distributed training both more communication-efficient and information-theoretically private, for both classical and quantum learning models. Ring all-reduce is the foundational communication primitive for large-scale distributed training. We present a quantum version that reduces per-link online communication by a provably optimal factor of two using pre-shared entanglement and superdense coding, without requiring the learning model or gradient computation to change. Beyond bandwidth, the primitive enables privacy guarantees that are information-theoretically impossible for any classical protocol, achieving composable ε-secure aggregation, via verified entanglement, at a 2x overhead in GHZ copies. Our hybrid quantum-classical communication architecture yields simultaneous communication and security advantages for large scale distributed training, regardless of whether the learning itself is quantum or classical. Finally, we characterise quantum advantages in gradient conflict detection for server-to-client communication under bandwidth constraints, a setting that arises after ring all-reduce is completed, when full gradient broadcast to external clients is infeasible. Two variants of the problem admit different separations. For margin-based alignment testing (\textsc{GapIP}_τ), the quantum advantage is quadratic in the margin parameter: \widetilde{O}(τ^{-1}\log P) qubits versus \widetilde{O}(\min(\τ^{-2},P)) bits. For sign-consistency auditing against a private parameter matching (\textsc{TieAudit}_ε), the advantage represents an exponential separation in communication complexity: Ω(\sqrt{P}) bits whereas O(ε^{-2}\log P) qubits suffice.
As sixth-generation (6G) wireless networks come into existence, there is more ever-growing demand for high-speed and secure communication than ever before. Conventional cryptographic methods are facing growing constraints to keep pace with the challenging reality of cyberattacks. Quantum-based communication technologies like quantum key distribution (QKD), quantum encryption, and quantum-resistant protocols provide an inherently secure means of transmitting data. This study examines how quantum communication technologies can be embedded within 6G architectures to provide enhanced data protection. With the help of AI and machine learning, 6G networks are able to anticipate threats and counter them, and quantum technologies offer unbreakable physical-layer encryption. Proposed work examines the security, architectural requirements, and implementation practicalities of this integration. In addition, we discussed the potential effect of quantum computing on present cryptographic techniques and describe future research directions for deployment at scale of quantum-secure communication in next-generation wireless systems.