Underwater quantum communication (UQC) has emerged as a promising approach for secure information transfer in oceanic environments by exploiting fundamental quantum mechanical principles. However, realizing UQC in practice remains highly challenging due to the inherently dynamic nature of the underwater optical channel, which is subject to wavelength-dependent absorption, multiple scattering, turbulence, and ambient noise—effects that collectively degrade photon transmission and induce quantum state errors. Conventional analytical models are insufficient to capture these nonlinear, time-varying channel characteristics, motivating the integration of artificial intelligence (AI) methods. Unlike previous surveys that focus narrowly on either underwater optical communications or quantum cryptography in isolation, this review uniquely synthesizes advances across quantum information science, ocean optics, and machine learning to address UQC-specific channel impairments. Specifically, we critically evaluate AI-driven approaches in three areas: (i) spatio-temporal deep learning models for data-driven channel estimation, demonstrating measurable improvements in channel prediction accuracy under realistic turbulence conditions; (ii) intelligent noise reduction and quantum error correction methods that reduce qubit error rates in high-scattering environments; and (iii) adaptive system optimization through reinforcement learning and hybrid optimization strategies that enhance operational fidelity and robustness. Based on this analysis, we identify key gaps in the field—including the absence of standardized underwater quantum datasets, limited experimental validation of AI-quantum hybrid frameworks, and the lack of end-to-end AI-quantum design tools—and outline concrete directions for future research. This review serves as a structured reference for researchers and practitioners working toward deployable, intelligent UQC systems.
Underwater quantum communication (UQC) has emerged as a promising approach for secure information transfer in oceanic environments by exploiting fundamental quantum mechanical principles. However, realizing UQC in practice remains highly challenging due to the inherently dynamic nature of the underwater optical channel, which is subject to wavelength-dependent absorption, multiple scattering, turbulence, and ambient noise—effects that collectively degrade photon transmission and induce quantum state errors. Conventional analytical models are insufficient to capture these nonlinear, time-varying channel characteristics, motivating the integration of artificial intelligence (AI) methods. Unlike previous surveys that focus narrowly on either underwater optical communications or quantum cryptography in isolation, this review uniquely synthesizes advances across quantum information science, ocean optics, and machine learning to address UQC-specific channel impairments. Specifically, we critically evaluate AI-driven approaches in three areas: (i) spatio-temporal deep learning models for data-driven channel estimation, demonstrating measurable improvements in channel prediction accuracy under realistic turbulence conditions; (ii) intelligent noise reduction and quantum error correction methods that reduce qubit error rates in high-scattering environments; and (iii) adaptive system optimization through reinforcement learning and hybrid optimization strategies that enhance operational fidelity and robustness. Based on this analysis, we identify key gaps in the field—including the absence of standardized underwater quantum datasets, limited experimental validation of AI-quantum hybrid frameworks, and the lack of end-to-end AI-quantum design tools—and outline concrete directions for future research. This review serves as a structured reference for researchers and practitioners working toward deployable, intelligent UQC systems.
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.
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.
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 rapid convergence of quantum computing and artificial intelligence is reshaping computational paradigms across cryptography, optimization, sensing, and intelligent systems. This paper surveys and synthesizes recent developments at the intersection of quantum computing and AI, with particular attention to superconducting quantum hardware, quantum reinforcement learning, quantum key distribution optimization, and hybrid AI-driven post-quantum security frameworks. Drawing upon 2026 literature — including advances in high-speed predistortion architectures for flux-bias lines [1], enhanced quantum key distribution parameter optimization [2], quantum reinforcement learning for spectrum allocation in 6G networks [3], hybrid AI–post-quantum encryption for Internet of Medical Things (IoMT) systems [4], and measurement-consistent low-field MRI enhancement via k-space noise modeling [5] — we present a unified perspective on how quantum resources augment classical AI and how AI, in turn, accelerates the practical deployment of quantum technologies. We introduce a conceptual Quantum–AI Convergence Framework that maps hardware constraints, algorithmic opportunities, and security requirements. The analysis highlights persistent challenges in noise resilience, scalability, hybrid orchestration, and regulatory readiness, while outlining promising research directions for trustworthy quantum-enhanced intelligence.
Abstract This review aimed to explore the integration of Quantum Computing (QC) with Artificial Intelligence (AI) subsets such as Machine Learning (ML) and Deep Learning (DL), addressing the computational demands posed by the exponential growth of visual data. It identifies key challenges such as interdisciplinary complexity, lack of standard benchmarks, scalability, integration barriers, and the theoretical-practical gap in quantum applications. The review systematically examines existing literature on the application of quantum algorithms in areas including image processing, Natural Language Processing (NLP), Transfer Learning (TL), Federated Learning (FL), networking, cybersecurity and the finance sector. It highlights the usage of quantum principles like superposition and entanglement to accelerate computations, optimize models, and enhance data security in ML/DL frameworks. Findings indicate that integrating QC with ML/DL offers faster convergence, improved optimization, secure decentralized learning, and efficient handling of large-scale and complex data. Specific improvements are observed in TL and FL approaches, NLP accuracy, cryptographic robustness, and performance in medical diagnostics and autonomous systems. QC holds transformative potential in enhancing ML/DL capabilities across domains. Despite existing challenges such as error mitigation and integration complexity, its combination with classical learning methods opens new frontiers for research in AI-driven sectors. Future studies should focus on bridging theoretical and application-level gaps while creating standardized evaluation frameworks.
Abstract The emerging era of quantum computing has highlighted the importance of secure communication systems that combine quantum-resistant cryptography, quantum communication, and advanced security analytics. This systematic review critically examines hybrid models of quantum and classical artificial intelligence, focusing on architectures for quantum key distribution (QKD), intrusion detection, network management, and the integration of post-quantum cryptography. Following PRISMA 2020 guidelines, studies from January 2020 to July 2026 were sourced from IEEE Xplore, ACM Digital Library, ScienceDirect, SpringerLink, Wiley Online Library, and backward citation searches. Out of these, 30 primary studies met the inclusion criteria and were evaluated using an eight-item quality rubric and a five-level evidence-maturity framework. The findings are grouped into three categories: AI-enhanced QKD and secure communication, hybrid quantum–classical learning for intrusion detection, and post-quantum or hybrid cryptographic solutions. AI-supported QKD research has shown notable reductions in parameter search time while maintaining near-optimal secret-key rates, primarily in simulation settings. Hybrid quantum machine learning models showed competitive intrusion detection accuracy and F1 scores, but improvements over classical methods were modest, dataset-dependent, and often lacked comprehensive reporting on false positives, statistical significance, or computational costs. Post-quantum cryptography approaches demonstrated greater maturity, with evaluations involving Transport Layer Security (TLS), Internet Protocol Security (IPsec), embedded devices, wireless links, and hardware accelerators, although performance varied with platform resources, cryptographic object sizes, network conditions, and side-channel protections. Among the studies, half were simulation-based, 30% involved laboratory experiments, and 20% were functional prototypes; none showed sustained operational deployment of an integrated hybrid quantum–classical AI security system. The review concludes that current evidence supports application-specific feasibility rather than universal quantum advantage. Achieving practical deployment requires standardized benchmarking, comparable classical baselines, transparent cost analysis, hardware validation, interoperable protocols, and longer-term field testing.