Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connections and takes advantage of the lower loss limit in space, which makes it more efficient. This work surveys the recent development in Quantum Satellites and Quantum UAVs-based networks. Here, the importance of the latest technologies, including quantum artificial intelligence, blockchain quantum machine learning, quantum satellites and quantum UAVs, are explored from network perspectives. Further, this work discussed the role of satellite-based images and artificial intelligence.
Quantum communication is the most secure technique of transmitting data available today. Fiber communication lines and satellite-to-ground links have served as the basis for the most successful quantum networks that have been developed so far. Using a UAV, satellite or both for free-space quantum communication reduces the need for permanent ground connections and takes advantage of the lower loss limit in space, which makes it more efficient. This work surveys the recent development in Quantum Satellites and Quantum UAVs-based networks. Here, the importance of the latest technologies, including quantum artificial intelligence, blockchain quantum machine learning, quantum satellites and quantum UAVs, are explored from network perspectives. Further, this work discussed the role of satellite-based images and artificial intelligence.
Due to recent advancements in quantum drones, the Internet of Quantum Drones (IoQDs), and Drone-to-Satellite connectivity, several advantages have been anticipated for real-time applications. This work examines quantum computing issues, including quantum data processing, techniques, circuits, and algorithms important for quantum drones or their networks. Here, we discussed the current research trends on quantum computing, quantum-safe computing, or post-quantum cryptography important to quantum networks, followed by the numerous advantages, limitations, future advancements, and research issues connected with quantum technologies, drones, and their network. This work has also prepared a taxonomy of quantum-related areas depending upon the logic of their learning, followed by a review of each of these areas. We review the most recent work over quantum algorithms used in various-quantum-related areas and networks, the role of quantum satellites for drone-based networks and communications, how quantum artificial intelligence and quantum machine learning are important for quantum drones, networks and futuristic applications, quantum attacks, quantum genetic algorithms, and the importance of post-quantum and quantum-safe cryptography. The challenges and research directions in these domains are explored as well. Lastly, this work presents an overview of the current state of knowledge in various promising technologies that are recently found to be important for quantum drones and networks.
The rapid evolution of autonomous systems—such as Unmanned Ground Units (UGUs), Unmanned Aerial Vehicles (UAVs), and drone networks—has generated unprecedented demands for secure, intelligent, and efficient data processing. This study presents a comprehensive review of recent advancements integrating Quantum Machine Learning (QML) and blockchain technologies to address these challenges. Following a systematic review approach, relevant studies published between 2020 and 2024 were analyzed to evaluate their contributions to enhancing decision-making, communication reliability, and data protection in autonomous networks. The findings reveal that QML enables adaptive learning and optimization in dynamic environments, while blockchain ensures data immutability, decentralized trust, and robust security. Together, these technologies offer a promising foundation for next-generation autonomous systems characterized by transparency, resilience, and computational efficiency. The paper concludes by outlining open research issues, including scalability, interoperability, and quantum resource constraints, and highlights future research opportunities for developing unified QML–blockchain frameworks in autonomous networks.
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 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.
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.
The two emerging technologies are machine learning and artificial intelligence, and as we all know, quantum computing is one of the most revolutionary developments in technology. Researchers are considering combining machine learning and quantum computing with the advancements in both fields. As a result, quantum machine learning-a fusion of these two fields-has evolved. It has the capacity to effectively address a wide range of real-world issues. Both fields will surely benefit from the combined results of the two fields. When the potential of quantum principles and its peculiarities is employed with machine learning, quantum machine learning reaches a very advanced level. Now that quantum concepts are being incorporated, traditional machine learning algorithms like SVM, PCA, and KNN are being reviewed as QSVM, Q-KNN, and QPCA, which are the most effective and powerful techniques for quantum machine learning. Quantum machine learning is a boom for the future; it will soon vastly surpass even the most advanced neural networks, deep learning systems, and machine learning systems of today. Modern machine learning is quicker than classical computing thanks to quantum machine learning, which is concerned with quantum software. Quantum data from an artificial quantum system is what QML is based on. In this paper, we will also study about the future network that is quantum network, which will be use by replacing classical networking. By enabling quantum communication between any two sites on Earth, a quantum internet is intended to fundamentally advance internet technology.