Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.
Quantum communication and quantum sensing, which leverage the unique characteristics of quantum systems, enable information-theoretically secure communication and high-precision measurement of physical quantities. However, they both face numerous challenges on the path to practical application. Artificial intelligence (AI), as a major technological advancement in current scientific landscape, offers powerful data processing and analytical capabilities, providing new ideas and methods for optimizing and enhancing quantum communication and sensing systems. In quantum communication, AI techniques have greatly improved the performance and security of quantum key distribution, quantum memory, and quantum networks through parameter optimization, real-time feedback control, and attack detection. In quantum sensing, quantum sensing technology enables ultra-high sensitivity detection of physical quantities such as time and magnetic fields. The introduction of AI has opened up new avenues for achieving high-precision and high-sensitivity quantum measurements. With AI, sensor performance is optimized, and measurement accuracy is further enhanced through data analysis. This paper also analyzes the current challenges in using AI to empower quantum communication and sensing systems, such as implementing efficient algorithm deployment and system feedback control under limited computational resources, and addressing complex task environments, dynamically changing scenarios, and multi-task coordination requirements. Finally, this paper discusses and envisions future development prospects in this field.
Quantum sensing, with measurement performance exceeding classical limits, boasts breakthrough application potential across multiple fields but faces traditional bottlenecks like device design, state manipulation, and signal processing. This paper focuses on the interdisciplinary area of AI (Artificial Intelligence)-empowered quantum sensing, systematically reviewing AI’s application progress in three core aspects: quantum sensor structure design, quantum state manipulation, and signal processing. It analyzes typical cases to interpret AI’s solutions to high-dimensional optimization and complex noise adaptation, extracting a data-driven technical paradigm that improves quantum sensors’ sensitivity, stability, and integration. Additionally, it identifies common challenges such as model interpretability and data dependence, and envisions future directions like physics-informed models and quantum-classical fusion, providing references for quantum sensing’s practicalization and next-generation intelligent system development.
The convergence of quantum photonics and artificial intelligence (AI) is redefining the landscape of optical metrology, sensing, and communication, enabling unprecedented precision, adaptability, and data processing capabilities. This paper explores the synergistic integration of AI-driven algorithms with quantum photonic systems, highlighting transformative advancements such as machine learning-enhanced quantum state estimation, intelligent control of photonic circuits, and adaptive quantum error correction. Emphasis is placed on how AI facilitates real-time decision-making and noise mitigation in complex quantum environments, thereby enhancing the sensitivity and resolution of optical sensors and metrological instruments. Additionally, the study investigates AI-assisted quantum communication protocols that optimize entanglement distribution, secure key generation, and photonic resource management. By bridging theoretical insights with emerging experimental frameworks, this work presents a comprehensive perspective on the mutual reinforcement between quantum photonics and AI, outlining their collective potential to drive the next generation of ultra-precise, intelligent optical technologies.
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.
The field of Quantum Artificial Intelligence (Quantum AI) has emerged as a promising interdisciplinary research area where the computational power of quantum computing is merged with the learning and decision-making power of AI. The application of quantum principles such as superposition, entanglement and quantum interference to machine learning algorithms has created new possibilities for solving problems that may be difficult to solve by classical machines. In the last few years, advances in Noisy Intermediate-Scale Quantum (NISQ) devices have helped to speed advances in Quantum Machine Learning (QML) algorithms, Quantum Neural Networks (QNNs), Quantum Support Vector Machines (QSVMs), Quantum Convolutional Neural Networks (QCNNs) and hybrid learning algorithms. This review provides a comprehensive overview of the research in the field of Quantum AI from 2020 to 2026, covering the key concepts, novel architectures, algorithmic advancements, and practical applications. A critical assessment of the application of Quantum AI in fields such as healthcare, finance, cybersecurity, scientific computing and optimization problems is provided, and the benefits of quantum-enhanced learning models are discussed. In addition, common difficulties such as quantum hardware limitations, sensitivity to noise, encoding of data, scalability constraints and a lack of speed in training variational quantum circuits are discussed. The review also covers new avenues of research, including trustworthy quantum machine learning, explainable Quantum AI, quantum federated learning, and fault-tolerant quantum computing. The results suggest while practical large-scale deployment is currently limited by existing hardware capabilities, hybrid quantum-classical systems have great potential to rapidly move intelligent data processing and develop next-generation AI systems.
On the integration of artificial intelligence with quantum sensing technologies for environmental monitoring, these make it much more precise, efficient and real time information end-user.In order to combat climate change markers, insulation effectiveness, and environmental hazard immediate response systems, this research dictates an AI based quantum sensing framework.Quantum augmented machine learning methods, i.e., Quantum Bayesian Networks and Quanvolutional Neural Networks, are used to improve accuracy of measurements from sensors and furthermore sensor calibration.In addition, the proposed system incorporates Q-SCALE for advanced sensor calibration to eliminate noise and improve sensor performance.The Quantum Wireless Sensing framework, in the form of quantum-based sensors, has an advantage in sensing dynamic environmental factors with an increased sensitivity.Some key findings include that AI quantum AI sensors are better than traditional sensors at alerting to abrupt environmental shifts, monitoring pollutants, testing greenhouse conditions, etc.Quantum entanglement was not used, and quantum sensors also achieved secure data transmission of distances beyond 50 kilometers.The proposed system achieves real-time data acquisition using lightweight models optimized for such resource constrained environments and reduces both the energy consumption.This major step in research will lead towards development of more accurate as well as scalable, sustainable environmental monitoring solutions with enhanced data security.As such, recent innovations such as AI coupled with nanomaterials and IoT-instigated quantum surveillance systems indicate the extent of this field's capability.