With the rapid advancement of drone technology, the demand for high-quality and real-time video streaming from drones has significantly increased. However, existing classical computing techniques face numerous challenges in meeting the requirements of efficient video encoding, transmission, and decoding. Quantum computing has emerged as a promising approach to address these limitations by leveraging quantum algorithms and principles to enhance various aspects of video streaming systems. This paper presents a taxonomy for the use of quantum computing in drone video streaming technology. The taxonomy is designed to categorize and classify the diverse applications of quantum computing in the different stages of drone video streaming, including video compression, encryption, transmission, and decoding. The taxonomy framework encompasses both theoretical and practical aspects, considering the current state of quantum computing technologies and their potential impact on drone video streaming. The taxonomy is structured into several key dimensions, including quantum algorithms, quantum- enhanced data compression techniques, quantum encryption methods, and quantum-assisted transmission and decoding strategies. Each dimension is further divided into subcategories that explore specific approaches and methodologies relevant to quantum computing in drone video streaming. Furthermore, the paper highlights the benefits and challenges associated with the adoption of quantum computing in this domain. It discusses how quantum computing can improve video compression efficiency, enhance data security through quantum encryption algorithms, enable faster and more reliable transmission using quantum-assisted techniques, and facilitate real-time video decoding with the aid of quantum algorithms. Through this taxonomy, researchers and practitioners in the field of drone video streaming can gain a comprehensive understanding of the potential applications and implications of quantum computing. It serves as a valuable reference for exploring innovative solutions and design considerations to harness the power of quantum computing in improving the performance and capabilities of drone video streaming systems.
With the rapid advancement of drone technology, the demand for high-quality and real-time video streaming from drones has significantly increased. However, existing classical computing techniques face numerous challenges in meeting the requirements of efficient video encoding, transmission, and decoding. Quantum computing has emerged as a promising approach to address these limitations by leveraging quantum algorithms and principles to enhance various aspects of video streaming systems. This paper presents a taxonomy for the use of quantum computing in drone video streaming technology. The taxonomy is designed to categorize and classify the diverse applications of quantum computing in the different stages of drone video streaming, including video compression, encryption, transmission, and decoding. The taxonomy framework encompasses both theoretical and practical aspects, considering the current state of quantum computing technologies and their potential impact on drone video streaming. The taxonomy is structured into several key dimensions, including quantum algorithms, quantum- enhanced data compression techniques, quantum encryption methods, and quantum-assisted transmission and decoding strategies. Each dimension is further divided into subcategories that explore specific approaches and methodologies relevant to quantum computing in drone video streaming. Furthermore, the paper highlights the benefits and challenges associated with the adoption of quantum computing in this domain. It discusses how quantum computing can improve video compression efficiency, enhance data security through quantum encryption algorithms, enable faster and more reliable transmission using quantum-assisted techniques, and facilitate real-time video decoding with the aid of quantum algorithms. Through this taxonomy, researchers and practitioners in the field of drone video streaming can gain a comprehensive understanding of the potential applications and implications of quantum computing. It serves as a valuable reference for exploring innovative solutions and design considerations to harness the power of quantum computing in improving the performance and capabilities of drone video streaming systems.
Quantum computing in drone technology begins to show potential big time, especially in the possibility of data processing, communication, and how to bring operational efficiency.In this study, the integration of quantum algorithms on drone navigation systems to enhance their efficiency in terms of security and performance is being investigated.However, in the interest of full disclosure, it should be noted that quantum-enabled drones have not been built and tested elsewhere to get an insight from a practical standpoint. This research is based on simulations and hypothetical models, which show that quantum algorithms could improve the optimization of drone operations.A simulation performed showed that the mean path length of data transmission was shortened by 12.4%, both from source to destination and back, compacting the whole process and increasing speed while it decreased packet loss by an average value close to 75% — when compared with regular methods. High-density urban obstacle avoidance efficiency increased by 26.1%. Though these results demonstrate an impressive theoretical advantage, realizing practical quantum computing on drones has many challenges to be overcome such as miniaturization of hardware components, power consumption and environmental protection. Overcoming these challenges is critical for future explorations into what quantum-integrated drones can achieve.This article presents a holistic overview of the current status of quantum technological advances in drones and sketches out what constitutes crucial steps to make things turn from theoretical calculations to real-life operational systems. Accordingly, this study sets a new foundation for future advancements in this nascent field and offers solutions that could get one step closer to realizing ambitious visions of autonomous aerial systems across multiple domains.
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
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 advancement of quantum computing has, without question, outpaced the rate of the subsequent setting up of an appropriate framework to categorize its various technologies. This has resulted in essentially fragmented terminologies with research efforts being considered in isolation, limiting collaboration or adaptation by industry. This chapter intends to fill the gap by introducing a multidimensional taxonomic approach to help develop a coherent and standardized framework for terminology in quantum computing. The proposed framework separates itself from existing frameworks by unifying four significant dimensions: computational models, hardware implementations, algorithmic strategies, and application domains. Its modular nature promotes relative ease of adaptation to emerging technologies, such as diamond-based qubits and cloud-based quantum platforms. The theoretical framework upon which this model has been developed is based on systematic literature review, domain analysis, and expert consultation. Using comparative benchmarking and real-world scenarios, the framework documents its flexibility in coordinating technical capabilities with market demands. For academia, it offers a structured curriculum tool and research roadmap, whereas, for industry, it clarifies investment priorities and benchmarks technological advancements. The identified challenges such as complexity and maintenance demands, are acknowledged with solutions proposed through dynamic updating mechanisms and collaborative governance. Providing a common terminology and adaptable structure not only organizes present knowledge but also makes possible a faster transfer of theoretical breakthroughs to social impact. It allows stakeholders to conduct complex tasks on quantum computing technology and ensure equitable and sustainable utilization of its future transformative potential.
The rapid growth of digital communication and video transmission has brought about an urgent need for robust encryption methods to ensure the security and integrity of video data. Traditional encryption techniques, while effective, face increasing challenges from advancing computing capabilities. Quantum cryptography has emerged as a promising solution to secure video transmission. In this article, we propose a novel cutting-edge Hybrid Quantum Video Encryption Framework. Our key idea is to integrate the strengths of quantum encryption and the classical video transmission paradigm to enhance the security of video transmission. By combining the proposed quantum encryption framework which uses a pseudorandom number generated key to perform a row-wise XOR operation along with the secure SSL-encrypted HTTP transmission over the internet, this framework provides a robust defense against threats. The experimental simulation conducted under the new computational integrated paradigm shows the strong encryption capability of the proposed method. The superior performance of the proposed method as validated through statistical analysis, outperforms the state-of-the-art video encryption frameworks in terms of Information Entropy and Correlation Coefficient for encrypted plain images offering a significant advancement in the field of secure video transmission.
Quantum computing, with its promise of exponential speedups and novel algorithms, has emerged as a revolutionary technology in various computational fields, including image processing. This survey provides a comprehensive review of the applications of quantum computing in image processing, exploring how quantum algorithms and hardware can address classical challenges such as image compression, enhancement, pattern recognition, and image recovery. The paper delves into the theoretical foundations of quantum computing, discusses current advancements, and compares quantum methods with traditional approaches. Moreover, it identifies the key challenges, such as scalability and hardware limitations, that currently hinder the widespread adoption of quantum techniques in image processing. Finally, the survey outlines future research directions, highlighting the potential for further integration of quantum computing with advanced image processing technologies like deep learning. This work aims to serve as a foundational reference for researchers and practitioners interested in the intersection of quantum computing and image processing.