Quantum science and quantum technology have become significant areas that have the potential to bring up revolutions in various branches or applications including aeronautics and astronautics, military and defense, meteorology, brain science, healthcare, advanced manufacturing, cybersecurity, artificial intelligence, etc. In this study, we present the advances and trends of quantum technology. Specifically, the advances and trends cover quantum computers and Quantum Processing Units (QPUs), quantum computation and quantum machine learning, quantum network, Quantum Key Distribution (QKD), quantum teleportation and quantum satellites, quantum measurement and quantum sensing, and post-quantum blockchain and quantum blockchain. Some challenges are also introduced.
Quantum science and quantum technology have become significant areas that have the potential to bring up revolutions in various branches or applications including aeronautics and astronautics, military and defense, meteorology, brain science, healthcare, advanced manufacturing, cybersecurity, artificial intelligence, etc. In this study, we present the advances and trends of quantum technology. Specifically, the advances and trends cover quantum computers and Quantum Processing Units (QPUs), quantum computation and quantum machine learning, quantum network, Quantum Key Distribution (QKD), quantum teleportation and quantum satellites, quantum measurement and quantum sensing, and post-quantum blockchain and quantum blockchain. Some challenges are also introduced.
Quantum computing has the potential to offer new capabilities and quantum computers will achieve breakthroughs in finance, cybersecurity, medical science and healthcare, defense and military missions, etc. Progress in quantum science and quantum technology are introduced in this paper that specifically include quantum computers, quantum devices, quantum processing units (QPU), quantum artificial intelligence (QAI), quantum machine learning (QML), quantum cloud computing, quantum network and communication, quantum teleportation, quantum experiment satellite, quantum cryptography, and quantum key distribution (QKD). Some challenges are also presented.
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 article focuses on quantum technologies and the prospects for their application in the defense and security sectors. Based on an analysis of the current state of development of quantum technologies and their applications in defense and security, three main areas are identified: quantum computing, quantum communication, and quantum sensors. For each of these, a specific set of applications, advantages, and limitations is identified. It is noted that quantum computing in defense and security finds application primarily in four subfields: post-quantum cryptography, quantum cryptography, cyber threat detection, and cryptanalysis; quantum communication ensures secure information transmission by utilizing the physical properties of quantum states, particularly the principle of the impossibility of cloning a quantum state, with its primary practical application being Quantum key distribution and its promising application being the quantum internet; quantum sensors are the category of quantum technologies closest to operational deployment, as they utilize the hypersensitivity of quantum states to external disturbances to achieve measurement accuracy unattainable by classical devices. A summary of the main areas of application for quantum technologies in defense and security indicates that quantum sensors have the nearest practical implementation horizon (5–7 years), while quantum computing will have the greatest strategic impact on cryptographic infrastructure due to the threat it poses to existing asymmetric encryption algorithms—which is precisely why the transition to post-quantum standards is becoming urgent. Therefore, given the significant interest in and funding for quantum technologies from both the civilian industry and governments, it is expected that these technologies will continue to develop and new quantum applications will become available over the next five to ten years, and new advances in the development of quantum technologies may bring new opportunities for the military, but for the military to actually reap the benefits of new quantum technologies, they need to actively engage in this field and guide the development and implementation of military applications of quantum technologies.
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 science and technology are increasingly recognised as critical areas of research and development with profound implications for various scientific fields and practical applications. Quantum science explains experimental observations of subatomic phenomena, thereby enhancing our fundamental understanding of the entire system of concepts based on the principles of quantum theory. Quantum computing has the potential to bring about a radical transformation in computational methods. The development of fundamental tools such as quantum chemistry, quantum cryptography, and quantum machine learning, along with advanced applications like quantum biology, quantum finance, quantum sensing, weather forecasting, and quantum space science, is expected to exert a significant global impact. This review provides a broad overview of developments and highlights the importance of research with diverse possible applications in the field, offering solutions across sectors
Quantum Computing (QC) and Artificial Intelligence (AI) have emerged as key technologies in the evolution of Industry 6.0, driving advancements in automation and advanced analytics, and process optimization. Their integration holds the potential to revolutionize sectors such as data science, healthcare, finance, and cybersecurity by enabling faster and more efficient computations through qubits, superposition, and quantum entanglement. However, the lack of structured knowledge regarding specific QC methodologies and applications in AI hinders its optimal implementation and development. Consequently, this study aims to identify the applications and variables associated with QC-AI integration. To this end, a systematic literature review was conducted following the PRISMA 2020 methodology, drawing on studies from Scopus and Web of Science databases. This enabled the analysis of trends, limitations, and opportunities in this technological convergence. This study aims to systematically examine the intersection of quantum computing and artificial intelligence by identifying the key technological features, integration requirements, and sectoral applications that define the current state of the field. The review contributes by mapping existing research, highlighting methodological approaches, and revealing gaps that may guide targeted advancements in hybrid quantum AI systems. The insights generated have the potential to accelerate innovation in high-impact domains such as healthcare, finance, energy, and cybersecurity. The findings indicate that the main advances in QC applied to AI focus on quantum optimization, Quantum Machine Learning (QML), and post-quantum cryptography. Notably, sectors such as energy, healthcare, and finance have shown significant progress in adopting these technologies. For example, in healthcare, QML has been applied to simulate molecular interactions to accelerate drug discovery, and in finance, it enhances predictive models for market behavior. The study concludes that although QC demonstrates substantial potential to enhance AI, its broader adoption remains constrained by reliance on NISQ hardware, the need for effective error correction, and the limited scalability of hybrid quantum classical algorithms. Addressing these challenges will be essential to establishing QML as a cornerstone of technological innovation and digital transformation. Additionally, this review introduces an integrative framework that categorizes key AI QC convergence dimensions and proposes a classification of application areas based on technical requirements and algorithmic capabilities. These contributions aim to guide future experimental validations and hybrid model development.