Quantum communication and machine learning convergence is one of the promising directions in the development of communication systems that could be secure, efficient, and scalable. The modern communication networks employ quantum communication technologies, such as quantum key distribution (QKD), entangement, and teleportation, that can ensure high security and allow the transmission of reliable data. Combined with machine learning, these technologies can be used to process data better, provide better security, and perform better in many applications, including the Internet of Things (IoT), intelligent communication networks, health care, and artificial intelligence. This paper will provide a review of the key principles of quantum communication and the application of machine learning in communication systems, and how the two concepts can be applied to advantage. Besides that, the paper discusses critical technical problems, such as scalability, system integration, and the unavailability of standardization, that constrain the present application of hybrid quantum-classical systems. In addition, the future research directions are mentioned, specifically quantum-enhanced federated learning and the construction of a quantum internet. In general, this paper offers an analytical and systematic review of the field, highlighting the existing challenges, as well as the opportunities that may be explored in the future to establish next-generation intelligent communication systems.
Quantum communication and machine learning convergence is one of the promising directions in the development of communication systems that could be secure, efficient, and scalable. The modern communication networks employ quantum communication technologies, such as quantum key distribution (QKD), entangement, and teleportation, that can ensure high security and allow the transmission of reliable data. Combined with machine learning, these technologies can be used to process data better, provide better security, and perform better in many applications, including the Internet of Things (IoT), intelligent communication networks, health care, and artificial intelligence. This paper will provide a review of the key principles of quantum communication and the application of machine learning in communication systems, and how the two concepts can be applied to advantage. Besides that, the paper discusses critical technical problems, such as scalability, system integration, and the unavailability of standardization, that constrain the present application of hybrid quantum-classical systems. In addition, the future research directions are mentioned, specifically quantum-enhanced federated learning and the construction of a quantum internet. In general, this paper offers an analytical and systematic review of the field, highlighting the existing challenges, as well as the opportunities that may be explored in the future to establish next-generation intelligent communication systems.
Abstract Artificial intelligence (AI) and classical machine learning (ML) techniques have revolutionised numerous fields, including quantum communication. Quantum communication technologies rely heavily on quantum resources, which can be challenging to produce, control, and maintain effectively to ensure optimum performance. ML has recently been applied to quantum communication and networks to mitigate noise‐induced errors and analyse quantum protocols. The authors systematically review state‐of‐the‐art ML applications to advance theoretical and experimental central quantum communication protocols, specifically quantum key distribution, quantum teleportation, quantum secret sharing, and quantum networks. Specifically, the authors survey the progress on how ML and, more broadly, AI techniques have been applied to optimise various components of a quantum communication system. This has resulted in ultra‐secure quantum communication protocols with optimised key generation rates as well as efficient and robust quantum networks. Integrating AI and ML techniques opens intriguing prospects for securing and facilitating efficient and reliable large‐scale communication between multiple parties. Most significantly, large‐scale communication networks have the potential to gradually develop the maturity of a future quantum internet.
The integration of Artificial Intelligence (AI) into the field of quantum communication has emerged in recent years as a promising avenue for enhancing secure data transmission, error correction, and scalability in quantum networks. This paper offers a comprehensive survey of AI applications in quantum communication, with a focus on machine learning (ML) models such as neural networks and reinforcement learning, which are adapted to manage complex quantum challenges. Despite the growing body of research, current studies often concentrate on specific AI techniques or quantum applications, lacking a unified framework that evaluates their practical effectiveness, scalability, and cost-efficiency. Addressing this gap, our survey systematically reviews and categorizes AI-driven methodologies across core aspects like system security, transmission speed, and reliability. Through a critical comparison and ranking of existing techniques, this study identifies best practices and key advancements while highlighting areas requiring further exploration. By providing an in-depth analysis of AI's potential to transform quantum communication, this paper aims to serve as a foundational resource for researchers and practitioners seeking to develop resilient, adaptive, and economically viable quantum communication systems.
Abstract - Quantum networking represents one of the most transformative developments in modern secure communication. Unlike classical networks that rely on mathematical encryption, quantum networks exploit the physical properties of qubits—superposition, entanglement, and the no-cloning theorem—to establish communication channels that are inherently tamper-evident. As quantum technologies mature, applications such as quantum key distribution (QKD), quantum repeaters, quantum teleportation, quantum memory systems, and quantum internet protocols are gaining global traction. This paper consolidates current progress in quantum networking technologies and provides an extended discussion of recent advancements in protocols, hardware components, integrated photonic systems, and machine-learning-enhanced architectures. Additionally, results synthesized from established literature, illustrate performance trends, security gains, and practical deployment challenges in quantum communication networks. The paper also outlines future directions toward scalable quantum internets, hybrid quantum-classical infrastructures, error-corrected qubit networks, satellite-based QKD, and intelligent routing systems. Overall, quantum networking is positioned to redefine the future of secure data transfer, distributed computing, and global communication infrastructures. Key Words: Quantum networking, Quantum Key Distribution (QKD), Entanglement Distribution, Integrated Photonic Chips, Machine Learning (ML)
Quantum communication and information networks offer unprecedented processing efficiency and security for data transfers. Technologies like quantum key distribution (QKD), quantum repeaters, quantum memory, and quantum entanglement sources are enabling technologies that ensure secure communication. QKD methods use quantum physics concepts to establish cryptographic keys, while quantum repeaters compensate for signal loss in fiber optic cables. Quantum memory enables storage and retrieval of quantum information, while quantum entanglement generators generate entangled photon pairs. Artificial intelligence and machine learning have significantly improved the efficacy and security of quantum communication. These methods can facilitate ultra-secure, reliable, large-scale communication and even a future quantum internet by analyzing quantum protocols and mitigating noise-induced errors. QKD protocols ensure secure communication channels, while quantum repeaters establish secure long-distance communication lines by mitigating signal loss. Traditional Internet technology is susceptible to surveillance due to Shannon's information theory and mathematics. The quantum internet (QI) uses satellite-based quantum communication and quantum cryptographic protocols for physical QI. Quantum entanglement sources fortify secure communication protocols, improving resistance to attacks. Quantum information networks (QINs) enhance computing, sensing, and security capabilities over extended distances. The development of reliable, robust communication systems depends on these technologies. Quantum-secured networks enhance urban security, smart management, and infrastructure protection by providing data privacy and facilitating secure data transmission, thereby facilitating robust infrastructure and technological development.
Secure, privacy-preserving sharing and analysis of health data is foundational to precision medicine, population health, and telehealth services. Emerging quantum networks promise fundamentally new communication primitives (entanglement, quantum key distribution) that when integrated with advanced AI architectures can enable end-to-end secure healthcare systems with improved confidentiality, tamper resistance, and novel distributed computation modes. This article develops a systematic framework for integrating quantum communications and cryptography with classical and hybrid quantum–classical AI models in healthcare. We (a) summarize the enabling quantum network technologies and AI building blocks, (b) propose layered system architectures and protocols for secure data exchange and collaborative learning, (c) formalize threat models and privacy requirements in healthcare contexts, (d) discuss algorithmic integrations (federated learning with QKD, quantum-enhanced machine learning, secure multiparty quantum protocols), (e) provide evaluation metrics and prototype deployment roadmaps, and (f) identify research gaps and regulatory implications. The treatment is scholarly yet practical, aimed at researchers, engineers, and healthcare stakeholders planning future-proof secure AI systems. (Keywords: quantum networks; quantum key distribution; quantum machine learning; federated learning; healthcare security; privacy.)
Quantum Machine Learning when fused with IoT systems holds capabilities of transforming the communication networks by providing optimum effectiveness, security and scalability. With the increased adoption of IoT systems, the monitoring techniques and data management, the need to facilitate real-time communication, and managing resources effectively become observations for IoT system developments. The computed-based machine learning algorithms do help in solving these problems, but these algorithms have their limitations due to the traditional classic systems. Regarding the intentions of this research, it is fundamental to highlight that the QML techniques will be centered on the capability that quantum algorithms have over processing and analyzing big amounts of data in IoT scenarios. This paper has demonstrated by means of QML how quantum parallelism and entanglement can enhance the key aspects of IoT systems, such as decisionmaking, information transfer, and correction. Also, quantum cryptographic techniques contain higher reliability providing confidentiality and indefectibility of sent information. This research focuses on diverse quantum algorithms including quantum clustering, classification, and reinforcement learning in IoT communication. The conducted simulations and practical case studies in the research show that QML is applicable and may provide benefits when integrated into IoT networks including more efficient networks, increased throughput, and security. These results point to the potential that QML might assume in defining the future of IoT communications, specifically in highly necessary and utilizing applications.