Automatic optical inspection (AOI) systems for drones are increasingly becoming core tools for intelligent operation and maintenance of industrial infrastructure. However, their traditional implementation paradigms face fundamental technical bottlenecks in communication security, collaborative control, and harsh environment perception. This paper aims to provide a systematic review and analysis of research progress in enabling nextgeneration UAV AOI systems through emerging quantum technologies, including quantum communication, quantum computing, and quantum sensing. The article first provides a comprehensive overview of the unique potential and current engineering challenges faced by technologies such as quantum key distribution (QKD), quantum approximate optimization algorithms (QAOA), and quantum imaging in addressing the limitations of classical systems. Furthermore, this analysis delved into the cutting-edge concept of a “quantum-classical hybrid architecture” that integrates quantum modules with mature classical systems, analyzing its integration mechanisms and performance gains across three dimensions: communication, computation, and sensing. The review indicates that while this fusion paradigm can theoretically significantly enhance system security, optimize efficiency, and improve robustness, its practical deployment remains constrained by the environmental sensitivity of quantum devices, high costs, and complex system integration. Finally, this paper offers a forward-looking discussion on future development directions in hardware miniaturization, algorithmic collaboration frameworks, and standardization research within this field, providing a framework for researchers to comprehensively understand the opportunities and challenges in this interdisciplinary domain.
Automatic optical inspection (AOI) systems for drones are increasingly becoming core tools for intelligent operation and maintenance of industrial infrastructure. However, their traditional implementation paradigms face fundamental technical bottlenecks in communication security, collaborative control, and harsh environment perception. This paper aims to provide a systematic review and analysis of research progress in enabling nextgeneration UAV AOI systems through emerging quantum technologies, including quantum communication, quantum computing, and quantum sensing. The article first provides a comprehensive overview of the unique potential and current engineering challenges faced by technologies such as quantum key distribution (QKD), quantum approximate optimization algorithms (QAOA), and quantum imaging in addressing the limitations of classical systems. Furthermore, this analysis delved into the cutting-edge concept of a “quantum-classical hybrid architecture” that integrates quantum modules with mature classical systems, analyzing its integration mechanisms and performance gains across three dimensions: communication, computation, and sensing. The review indicates that while this fusion paradigm can theoretically significantly enhance system security, optimize efficiency, and improve robustness, its practical deployment remains constrained by the environmental sensitivity of quantum devices, high costs, and complex system integration. Finally, this paper offers a forward-looking discussion on future development directions in hardware miniaturization, algorithmic collaboration frameworks, and standardization research within this field, providing a framework for researchers to comprehensively understand the opportunities and challenges in this interdisciplinary domain.
Background: High-precision sensing is vital for modern technology, yet conventional sensors are fundamentally constrained by classical noise limits. Quantum sensing leverages principles of quantum mechanics, such as superposition and entanglement, to overcome these barriers, offering unprecedented sensitivity. This review comprehensively analyzes the current state of quantum sensor technology, focusing on its underlying physical principles, emerging applications, and the pathway toward industrial maturation.Methods: We synthesize findings from key research areas, detailing the foundational quantum systems like Nitrogen-Vacancy (NV) centers in diamond and advanced optical techniques. The paper reviews the theoretical methods used to achieve sub-shot-noise performance and discusses the system-level engineering challenges necessary for practical deployment, particularly concerning microwave control and optical readout. We also assess the current landscape of standardization efforts for quantum technologies.Results: Significant breakthroughs are highlighted in both biomedical and environmental sensing. Applications range from tracking cellular redox-status using electron-paramagnetic resonance to ultra-sensitive salinity measurements. The analysis confirms that quantum sensing provides superior resolution compared to classical counterparts, unlocking new capabilities in in-vivo and real-time monitoring.Discussion: While offering transformative potential, widespread adoption faces hurdles, including the maintenance of quantum coherence in ambient environments and the high cost and complexity of current instrumentation. Future efforts must focus on miniaturization, integration with classical systems, and standardized fabrication protocols. Quantum sensing represents the next-generation technological paradigm for metrology.
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
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 computing advances pose a serious threat to conventional public-key cryptographic systems such as RSA and elliptic curve cryptography as quantum algorithms may undermine the computational assumptions that guaranty their security. This challenge has driven the development of quantumsafe security approaches among which Quantum Key Distribution (QKD) represents a fundamentally different paradigm based on quantum mechanical principles rather than mathematical complexity. This paper presents a structured review of recent progress in QKD based on a systematic analysis of literature between 2020 and 2025, we examine core protocols including discrete-variable, continuous-variable, measurement-deviceindependent, and twin-field approaches, while also discussing their relationship with post-quantum cryptography as a complementary strategy for future secure communication systems. We provide a unified comparison framework to support practical design and policy decisions by organizing protocols and implementations along deployment dimensions. The study analyze theoretical foundations, implementation technologies across fiber, free-space, satellite, and integrated photonic platforms, and key performance trade-offs involving scalability, security robustness, and deployment feasibility. Findings indicate substantial progress in transitioning QKD from laboratory research toward practical implementations, supported by advances in network architectures, integrated hardware and hybrid cryptographic designs. The comparative analysis will highlight distinct strengths and limitations among protocol families and emphasize the potential of combining QKD with post-quantum cryptographic methods to enhance resilience. Remaining challenges including scalability constraints, implementation-level vulnerabilities, performance limitations over long distances, and integration with existing communication infrastructures are critically discussed. Finally, emerging research directions such as quantum repeaters, hybrid quantum-classical security models, and exploratory directions such as artificial intelligence-assisted optimizations are identified as key factors in enabling the evolution of QKD toward largescale quantum-secure networking.
Objective: This research aims to evaluate and synthesise the development of artificial intelligence (AI) technologies integrated with quantum computing, especially regarding processing efficiency, hardware energy efficiency, and digital communication security. Research Design & Methods: This research utilizes the Systematic Literature Review (SLR) method of 13 scientific articles published in the ETRI Journal's 2024 special issue titled “Next-Gen AI and Quantum Technology.” The literature was selected based on inclusion criteria that included relevance to quantum AI, presence of experimental data, and contribution to computational efficiency. Findings: The study results show that approaches such as Quantum Reinforcement Learning and Quantum Kernel Classifiers can improve training efficiency and classification accuracy. Spiking Neural Networks technology reduced power consumption in AI-SoC and edge device designs. At the same time, the Quantum Key Distribution system demonstrated an error rate as low as 0.62% with WDM filter integration. The AONet video anomaly detection model achieves up to 97% AUC with the combination of a residual autoencoder and an attention module architecture. Implications & Recommendations: These findings indicate that quantum AI has great potential to overcome the limitations of classical computing in real-time applications and large-scale systems. However, challenges related to quantum noise, hardware stability, and integration with classical systems still need to be addressed. This research recommends strengthening hybrid infrastructure, developing interoperability standards, and utilizing multi-core architectures to support processing efficiency and data security. Contribution & Value Added: This study significantly contributes by systematically mapping existing methodologies and experiments in quantum AI, establishing a conceptual framework for future research avenues, and incorporating quantum AI technologies into industry, edge computing, and upcoming security systems
This chapter explores the transformative integration of Quantum Computing (QC) with the Internet of Things (IoT), highlighting significant advancements in security, optimization, and intelligent decision-making for cyber-physical systems. It provides a detailed review of quantum-enhanced algorithms, architectures, and sensing technologies designed for IoT ecosystems. Addressing the limitations of traditional methods in large-scale computation, network optimization, data analytics, and security, the chapter emphasizes core quantum concepts, i.e., superposition, entanglement, and interference. Techniques, including quantum annealing and the QAOA, enable efficient resource allocation, scheduling, and routing in emerging infrastructures like 6G and UAV networks. The use of hybrid quantum–classical models is examined for fault detection, cryptographic key generation, and real-time analytics across edge-to-cloud environments. QML has been shown to enhance adaptive learning and pattern recognition on resource-limited devices. To counter emerging cyber threats, quantum-resilient security solutions, including post-quantum cryptography, quantum random number generation, and quantum key distribution, are discussed. The chapter also highlights quantum sensors that utilize tunnelling, spin dynamics, and interference for applications in biomedical diagnostics, environmental monitoring, infrastructure evaluation, and autonomous navigation. Emerging use cases, including quantum-powered digital marketing and quantum-secured smart locks, are introduced. The chapter concludes by underscoring the transformative role of scalable hybrid QC–IoT architectures, which are poised to advance edge intelligence, smart infrastructure, healthcare, and logistics, while opening new directions for research and real-world deployment.