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.
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.
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 Artificial Intelligence (Quantum AI) embodies the integration of quantum computational paradigms with artificial intelligence frameworks to address the computational intractability and scalability constraints inherent in classical AI systems. By leveraging quantum mechanical properties such as superposition, entanglement, and quantum interference, Quantum AI enables enhanced representation learning, accelerated optimization, and improved probabilistic inference in high-dimensional problem spaces. In parallel, deep-tech innovations—including advanced deep learning architectures, neuromorphic computing, and edge-based intelligent processing—are redefining the architectural foundations of next- generation intelligent systems through energy-efficient, adaptive, and real-time computation. This paper presents a technically rigorous and comprehensive review of Quantum AI and deep-tech innovations, focusing on algorithmic formulations, architectural models, and system-level integration strategies. Core methodologies such as quantum machine learning algorithms, parameterized quantum circuits, variational quantum algorithms, and hybrid quantum–classical optimization frameworks are critically analysed with respect to computational complexity, repressibility, and feasibility on noisy intermediate-scale quantum (NISQ) hardware. Additionally, the role of neuromorphic computing in enabling spiking neural architectures and event-driven processing is examined as a complementary paradigm to quantum-enhanced learning models, particularly for low-power and latency-critical applications. The paper further investigates application- driven deployments of Quantum AI across domains including medical diagnostics, cryptographic security, autonomous decision-making, and smart cyber-physical systems, highlighting performance trade-offs and implementation constraints. Key challenges such as quantum noise, decoherence, data encoding bottlenecks, limited qubit connectivity, and ethical governance are systematically discussed. Finally, future research directions are identified, emphasizing fault-tolerant quantum learning architectures, scalable hybrid integration with deep-tech systems, and co-design methodologies for realizing practical next-generation intelligence platforms.
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.
Artificial intelligence (AI) and quantum information (QI) are rapidly co-evolving. AI is becoming a practical tool for learning, designing, controlling, and verifying quantum systems, while QI offers new computational models, representational structures, and learning-theoretic questions for AI. This survey reviews the interface from both directions. In the AI for QI direction, we organize recent progress around the central tasks of extracting information from limited measurements, training and discovering quantum algorithms, stabilizing noisy hardware, automating experimental and programming workflows, and extending learning-based methods to sensing and networking. In the QI for AI direction, we examine how quantum computation and quantum-inspired structures affect learning through algorithmic speedups, expressivity, trainability, generalization, neural-network design, and tensor-network representations. We close by identifying cross-cutting challenges in reproducibility, scalability, hardware realism, and co-design, arguing that progress will depend on tighter integration of theory, experiment, and hybrid quantum--classical systems.
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.
Quantum computing and artificial intelligence (AI) are two rapidly evolving technologies poised to redefine the future of computation and decision-making.The two fields unite under the name Quantum Artificial Intelligence (QAI) or Quantum Intelligence, which enables a transformative approach to problem-solving through the fusion of AI mental functions with quantum computing capabilities.This paper provides a comprehensive review of the current landscape, highlighting the opportunities and challenges in integrating quantum computing with AI systems.The research investigates current developments in quantum machine learning, quantum neural networks, and hybrid quantum-classical systems, which show promise for achieving rapid performance improvements and enhanced scalability and data processing capabilities.At the same time, it identifies critical barriers, including hardware limitations, algorithmic complexity, data encoding issues, and the lack of standardization in frameworks.The review further explores future directions, including the role of explainable AI, error correction, and interdisciplinary research in achieving practical quantum-AI synergy.By analyzing both the technological potential and the practical constraints, this paper aims to outline a roadmap for advancing quantum intelligence as a transformative force in next-generation computational paradigms.