Intelligent Underwater Quantum Communications: A Review of AI-Enabled Channel Estimation, Noise Mitigation, and System Optimization
Copyright © 2026 Vishal Sharma, Shweta Jain, Prashant Kumar. Published by Nan Yang Academy of Sciences Pte. Ltd.
This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.
Abstract
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.
Keywords: Underwater Marine Communications, Quantum Communication, Artificial Intelligence, Hybrid Networks, Climate-Aware Systems
References
[1] Theocharidis, T., Kavallieratou, E., 2025. Underwater Communication Technologies: A Review. Telecommunication Systems. 88(2), 54. DOI: https://doi.org/10.1007/s11235-025-01279-x
[2] Singh, D.P., Batham, D., 2022. A Review of Underwater Communication Systems. International Journal of Engineering Development and Research. 10(2), 100–104.
[3] Fang, C., Li, S., Wang, Y., et al., 2023. High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey. Photonics. 10(7), 811. DOI: https://doi.org/10.3390/photonics10070811
[4] Li, Z., Chitre, M., Stojanovic, M., 2025. Underwater Acoustic Communications. Nature Reviews Electrical Engineering. 2(2), 83–95. DOI: https://doi.org/10.1038/s44287-024-00122-w
[5] Li, Z., Li, W., Sun, K., et al., 2025. Recent Progress on Underwater Wireless Communication Methods and Applications. Journal of Marine Science and Engineering. 13(8), 1505. DOI: https://doi.org/10.3390/jmse13081505
[6] Ataa, M.S., Sanad, E.E., 2025. A Fast Secure and More Reliable Underwater Communication System Based on Light Fidelity Technology. Scientific Reports. 15(1), 14066. DOI: https://doi.org/10.1038/s41598-025-96484-8
[7] Jayaweera, V.L., Peiris, C., Darshani, D., et al., 2025. Visible Light Communication for Underwater Applications: Principles, Challenges, and Future Prospects. Photonics. 12(6), 593. DOI: https://doi.org/10.3390/photonics12060593
[8] Nadeem, R., Uma, V., 2026. Presenting the Methodologies, Challenges, and Opportunities in Underwater Target Recognition. In: Mittal, M., Gupta, S. (Eds.). Understanding AI Decisions, Computer Vision, and Management Analytics. IGI Global Scientific Publishing: Hershey, PA, USA. pp. 175–208. DOI: https://doi.org/10.4018/979-8-3693-8327-8.ch008
[9] Huang, Z., Liu, T., 2026. The Application of Marine Intelligent Platforms in Underwater Archaeology. Measurement. 275, 121373. DOI: https://doi.org/10.1016/j.measurement.2026.121373
[10] Zhang, Y., Liang, W., Huang, S., et al., 2025. Overview of Quantum Communication Based on the Ocean. In: Park, J.S., Camacho, D., Gritzalis, S., et al. (Eds.). Advances in Computer Science and Ubiquitous Computing: Proceedings of CSA 2024. Springer: Singapore. pp. 75–82. DOI: https://doi.org/10.1007/978-981-96-5693-6_12
[11] Zhou, X., Shen, A., Hu, S., et al., 2025. Towards Quantum-Native Communication Systems: State-of-the-Art, Trends, and Challenges. IEEE Communications Surveys & Tutorials. 28, 1436–1482. DOI: https://doi.org/10.1109/COMST.2025.3575842
[12] Usenko, V.C., Acín, A., Alléaume, R., et al., 2026. Continuous-Variable Quantum Communication. Reviews of Modern Physics. 98(1), 015003. DOI: https://doi.org/10.1103/mgj7-t6d3
[13] Wrona, K., Sobolewski, O., Sliwa, J., et al., 2024. Quantum-Resistant One-to-One Communication for Underwater Acoustic Networks. In Proceedings of the 2024 IEEE Military Communications Conference (MILCOM), Washington, DC, USA, 28 October–1 November 2024; pp. 190–195. DOI: https://doi.org/10.1109/MILCOM61039.2024.10773779
[14] Hufnagel, F., Sit, A., Grenapin, F., et al., 2019. Characterization of an Underwater Channel for Quantum Communications in the Ottawa River. Optics Express. 27(19), 26346–26354. DOI: https://doi.org/10.1364/OE.27.026346
[15] Allevi, A., Bondani, M., 2022. Towards Underwater Quantum Communication in the Mesoscopic Intensity Regime. Optics Express. 30(24), 44175–44185. DOI: https://doi.org/10.1364/OE.472034
[16] Mastriani, M., Iyengar, S.S., Latesh Kumar, K.J., 2021. Bidirectional Teleportation for Underwater Quantum Communications. Quantum Information Processing. 20(1), 22. DOI: https://doi.org/10.1007/s11128-020-02970-5
[17] Junejo, N.U., Sattar, M., Adnan, S., et al., 2023. A Survey on Physical Layer Techniques and Challenges in Underwater Communication Systems. Journal of Marine Science and Engineering. 11(4), 885. DOI: https://doi.org/10.3390/jmse11040885
[18] Hasan, S.R., Chowdhury, M.Z., Sayem, M., et al., 2023. Quantum Communication Systems: Vision, Protocols, Applications, and Challenges. IEEE Access. 11, 15855–15877. DOI: https://doi.org/10.1109/ACCESS.2023.3244395
[19] Massari, G., Marras, C., Cavallini, F., 2023. How Underwater Drones Can Promote the Environmental Sustainability of Offshore Natural Resources Exploitation. In Proceedings of the Offshore Technology Conference, Houston, Texas, USA, 1–4 May 2023. DOI: https://doi.org/10.4043/32481-MS
[20] Jain, S., Gandhi, A., Singla, S., et al., 2022. Quantum Machine Learning and Quantum Communication Networks: The 2030s and the Future. In Proceedings of the 2022 International Conference on Computational Modelling, Simulation and Optimization (ICCMSO), Pathum Thani, Thailand, 23–25 December 2022; pp. 59–66. DOI: https://doi.org/10.1109/ICCMSO58359.2022.00025
[21] Jain, S., Aggarwal, R., Gandhi, A.B., 2023. Quantum Chain of Things: The Quantum Triad—Integrating Blockchain, IoT and Quantum Computing for a Transcendent Future. In Proceedings of the International Conference on Computer Vision and Internet of Things 2023 (ICCVIoT'23), Coimbatore, India, 7–8 December 2023; pp. 299–304. DOI: https://doi.org/10.1049/icp.2023.2892
[22] Jain, S., Gandhi, A.B., Mehla, S., et al., 2024. Virtual Drive: Immersive VR-Controlled Car with Real-Time Interaction and Live Video Feed. In Proceedings of the 2024 International Conference on Computational Intelligence and Computing Applications (ICCICA), Samalkha, India, 23–24 May 2024; pp. 519–525. DOI: https://doi.org/10.1109/ICCICA60014.2024.10585050
[23] Jain, S., Bhandari, A.G., Prasad, D., et al., 2024. Quantum Chain of Things: Amalgamation of Blockchain (BC), Internet of Things (IoT), and Quantum Computing (QC) – QCOT Era. In Futuristic Trends in Information Technology V3B1. Iterative International Publishers: Chikmagalur, India. pp. 171–192. DOI: https://doi.org/10.58532/V3BBIT1P2CH2
[24] Wang, T., Dai, M., Guo, H., et al., 2025. When Quantum Empowerment Meets Marine Edge Computing: Challenges, Novel Paradigms, and Enabling Technologies. IEEE Network. 40(4), 40–48. DOI: https://doi.org/10.1109/MNET.2025.3637846
[25] Cao, J., Zhou, M., Niyato, D., et al., 2024. Entangled Optical Quantum Imaging Method Assisted by AUVs Based on Underwater Light Transmission Characteristics. IEEE Transactions on Vehicular Technology. 73(10), 15664–15668. DOI: https://doi.org/10.1109/TVT.2024.3402367
[26] Villaseñor, E., He, M., Wang, Z., et al., 2021. Enhanced Uplink Quantum Communication with Satellites via Downlink Channels. IEEE Transactions on Quantum Engineering. 2, 1–8. DOI: https://doi.org/10.1109/TQE.2021.3091709
[27] Huang, L., Wang, Y., Zhang, Q., et al., 2022. Machine Learning for Underwater Acoustic Communications. IEEE Wireless Communications. 29(3), 102–108. DOI: https://doi.org/10.1109/MWC.2020.2000284
[28] Niu, H., Li, X., Zhang, Y., et al., 2023. Advances and Applications of Machine Learning in Underwater Acoustics. Intelligent Marine Technology and Systems. 1(1), 8. DOI: https://doi.org/10.1007/s44295-023-00005-0
[29] Wang, Z., Du, J., Hou, X., et al., 2024. Toward Communication Optimization for Future Underwater Networking: A Survey of Reinforcement Learning-Based Approaches. IEEE Communications Surveys & Tutorials. 27(5), 2765–2793. DOI: https://doi.org/10.1109/COMST.2024.3505850
[30] Uyan, O.G., Akbas, A., Gungor, V.C., 2023. Machine Learning Approaches for Underwater Sensor Network Parameter Prediction. Ad Hoc Networks. 144, 103139. DOI: https://doi.org/10.1016/j.adhoc.2023.103139
[31] Haziq, M., Phung, Q.V., Lachowicz, S., et al., 2025. Modulation Techniques for Underwater Acoustic Communication: A Comprehensive Survey. IEEE Access. 13, 150715–150755. DOI: https://doi.org/10.1109/ACCESS.2025.3601799
[32] Jiang, Y., Xing, X., 2024. The Application of Artificial Intelligence in Unmanned Underwater Vehicle Communication Systems. Computers & Electrical Engineering. 117, 109288. DOI: https://doi.org/10.1016/j.compeleceng.2024.109288
[33] Das, S., Malik, P.K., Pandey, A., 2026. Challenges and Advances in Underwater Sonar Systems and AI-Driven Signal Processing for Modern Naval Operations: A Systematic Review. Journal of Field Robotics. 43(2), 899–931. DOI: https://doi.org/10.1002/rob.70077
[34] Lv, Z., Chen, D., Feng, H., et al., 2022. Artificial Intelligence in Underwater Digital Twins Sensor Networks. ACM Transactions on Sensor Networks. 18(3), 1–27. DOI: https://doi.org/10.1145/3519301
[35] Manzalini, A., 2020. Quantum Communications in Future Networks and Services. Quantum Reports. 2(1), 221–232. DOI: https://doi.org/10.3390/quantum2010014
[36] Alexeev, Y., Farag, M.H., Patti, T.L., et al., 2025. Artificial Intelligence for Quantum Computing. Nature Communications. 16(1), 10829. DOI: https://doi.org/10.1038/s41467-025-65836-3
[37] Nawaz, S.J., Sharma, S.K., Wyne, S., et al., 2019. Quantum Machine Learning for 6G Communication Networks: State-of-the-Art and Vision for the Future. IEEE Access. 7, 46317–46350. DOI: https://doi.org/10.1109/ACCESS.2019.2909490
[38] Pravin, S.C., Rohith, G., Kiruthika, V., et al., 2023. Underwater Animal Identification and Classification Using a Hybrid Classical-Quantum Algorithm. IEEE Access. 11, 141902–141914. DOI: https://doi.org/10.1109/ACCESS.2023.3343120
[39] Mafu, M., 2024. Advances in Artificial Intelligence and Machine Learning for Quantum Communication Applications. IET Quantum Communication. 5(3), 202–231. DOI: https://doi.org/10.1049/qtc2.12094
[40] Kakaraparty, K., Munoz-Coreas, E., Mahbub, I., 2021. The Future of mm-Wave Wireless Communication Systems for Unmanned Aircraft Vehicles in the Era of Artificial Intelligence and Quantum Computing. In Proceedings of the 2021 IEEE MetroCon, Hurst, TX, USA, 3 November 2021; pp. 1–8. DOI: https://doi.org/10.1109/MetroCon54219.2021.9666048
[41] Paglierani, P., Fahim Raouf, A.H., Pelekanakis, K., et al., 2023. A Primer on Underwater Quantum Key Distribution. Quantum Engineering. 2023(1), 7185329. DOI: https://doi.org/10.1155/2023/7185329
[42] Feng, Z., Li, S., Xu, Z., 2021. Experimental Underwater Quantum Key Distribution. Optics Express. 29(6), 8725–8736. DOI: https://doi.org/10.1364/OE.418323
[43] Li, D.D., Shen, Q., Chen, W., et al., 2019. Proof-of-Principle Demonstration of Quantum Key Distribution with Seawater Channel: Towards Space-to-Underwater Quantum Communication. Optics Communications. 452, 220–226. DOI: https://doi.org/10.1016/j.optcom.2019.07.037
[44] Mao, Y., Wu, X., Huang, W., et al., 2020. Monte Carlo-Based Performance Analysis for Underwater Continuous-Variable Quantum Key Distribution. Applied Sciences. 10(17), 5744. DOI: https://doi.org/10.3390/app10175744
[45] Nozari, M., Uysal, M., 2024. Deep Learning-Based Optimization of Underwater QKD Systems with Time-Gated SPADs. IEEE Access. 12, 117345–117353. DOI: https://doi.org/10.1109/ACCESS.2024.3444268
[46] Cao, J., Zhou, M., Zhang, R., et al., 2025. Robust Optical Quantum Imaging Framework with Entangled Photons in Oceanic Turbulent Environments. IEEE Transactions on Communications. 73(8), 6290–6305. DOI: https://doi.org/10.1109/TCOMM.2025.3538840
[47] Schetakis, N.A., Blazakis, K.V., Stavrakakis, G.S., et al., 2025. Quantum Machine Learning Applications in Ships: A Review. In: Spyrou, K.J., Themelis, N. (Eds.). Innovations in Sustainable Maritime Technology—IMAM 2025. Springer: Cham, Switzerland. pp. 149–159. DOI: https://doi.org/10.1007/978-3-032-02102-1_12
[48] Sambataro, O., Costanzi, R., Alves, J., et al., 2025. Current Trends and Advances in Quantum Navigation for Maritime Applications: A Comprehensive Review. IEEE Journal of Oceanic Engineering. 50(3), 2101–2134. DOI: https://doi.org/10.1109/JOE.2025.3538941
[49] Chouhan, S., Sandhu, R., Yadav, O.P., et al., 2025. Quantum-Powered Autonomous Deep-Sea Explorer Using AI, Microbiofuels, and Advanced Sensors for Oceanic Discovery. In Proceedings of the 2025 International Conference on Computer, Electrical & Communication Engineering (ICCECE), Kolkata, India, 7–8 February 2025; pp. 1–5. DOI: https://doi.org/10.1109/ICCECE61355.2025.10941009
[50] Parve, D., Obaidat, M.S., Panchal, K., et al., 2025. Secure Communication for Maritime Autonomous Surface Ships Using Quantum-Satellite Relays. In Proceedings of the 2025 IEEE Global Communications Conference, Taipei, China, 8–12 December 2025; pp. 2222–2227. DOI: https://doi.org/10.1109/GLOBECOM59602.2025.11432774
[51] Savcı, C., Akgül, T., Bıçakçı, B., 2025. COMA++: Quantum-Enhanced Communication in Multi-Agent Reinforcement Learning for Marine Debris Collection Optimization. In Proceedings of the 2025 10th International Conference on Computer Science and Engineering (UBMK), Istanbul, Türkiye, 17–21 September 2025; pp. 1339–1343. DOI: https://doi.org/10.1109/UBMK67458.2025.11206810
[52] Rezaei, T., Javadi, A., 2024. Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning. Journal of Environmental Management. 362, 121275. DOI: https://doi.org/10.1016/j.jenvman.2024.121275
[53] Xi, M., Wang, Z., He, J., et al., 2024. High-Precision Underwater Perception and Path Planning of AUVs Based on Quantum-Enhanced. IEEE Transactions on Consumer Electronics. 70(3), 5607–5617. DOI: https://doi.org/10.1109/TCE.2024.3449451
[54] Duangsuwan, S., Klubsuwan, K., 2025. Underwater Drone-Enabled Wireless Communication Systems for Smart Marine Communications: A Study of Enabling Technologies, Opportunities, and Challenges. Drones. 9(11), 784. DOI: https://doi.org/10.3390/drones9110784
[55] Bar, N.F., Yetiş, H., Özbey, N., et al., 2024. Quantum Computing Based Collaborative Optimum Mission Allocation Approach for Heterogeneous Multi Unmanned Vehicles. IEEE Access. 12, 169069–169078. DOI: https://doi.org/10.1109/ACCESS.2024.3498345
[56] Zuo, Z., Wang, Y., Mao, Y., et al., 2021. Security of Quantum Communications in Oceanic Turbulence. Physical Review A. 104(5), 052613. DOI: https://doi.org/10.1103/PhysRevA.104.052613
[57] Aldossary, M., 2025. Q-MobiGraphNet: Quantum-Inspired Multimodal IoT and UAV Data Fusion for Coastal Vulnerability and Solar Farm Resilience. Mathematics. 13(18), 3051. DOI: https://doi.org/10.3390/math13183051
[58] Prabhashana, S.C., Van Huynh, D., Duong, T.Q., 2025. Quantum DRL for UAV-RIS-Aided Maritime Communications with 6G Digital Twin Applications. In Proceedings of the 2025 IEEE International Conference on Communications Workshops (ICC Workshops), Montreal, QC, Canada, 8–12 June 2025; pp. 396–401. DOI: https://doi.org/10.1109/ICCWorkshops67674.2025.11162277
[59] Astiti, N.M., Lee, B.M., 2025. A Survey of Quantum Deep Reinforcement Learning for Resource Allocation in Future Wireless Networks. IEEE Communications Surveys & Tutorials. 28, 109–148. DOI: https://doi.org/10.1109/COMST.2025.3638435
[60] Surendran, S., Montresor, A., Vinodini Ramesh, M., 2025. A Reinforcement Learning Approach for Routing in Marine Communication Network of Fishing Vessels. SN Computer Science. 6(1), 62. DOI: https://doi.org/10.1007/s42979-024-03606-6
[61] Park, S., Kim, J.P., Park, C., et al., 2023. Quantum Multi-Agent Reinforcement Learning for Autonomous Mobility Cooperation. IEEE Communications Magazine. 62(6), 106–112. DOI: https://doi.org/10.1109/MCOM.020.2300199
[62] Chege, S., Walingo, T., 2023. Q-Learning-Based Underwater Sensor Networks Routing Protocol for Pollution Monitoring. In: Woungang, I., Dhurandher, S.K. (Eds.). The 6th International Conference on Wireless, Intelligent and Distributed Environment for Communication: WIDECOM 2023. Springer: Cham, Switzerland. pp. 127–141. DOI: https://doi.org/10.1007/978-3-031-47126-1_9
[63] Guangjie, L., 2026. QPSODRL: An Improved Quantum Particle Swarm Optimization and Deep Reinforcement Learning Based Intelligent Clustering and Routing Protocol for Wireless Sensor Networks. Scientific Reports. 16(1), 5526. DOI: https://doi.org/10.1038/s41598-026-35365-0
[64] Yang, H., Lin, K., Xiao, L., et al., 2024. Energy Harvesting UAV-RIS-Assisted Maritime Communications Based on Deep Reinforcement Learning Against Jamming. IEEE Transactions on Wireless Communications. 23(8), 9854–9868. DOI: https://doi.org/10.1109/TWC.2024.3367034
[65] Liu, J., Luo, H., Tao, H., et al., 2025. JLOS: A Cooperative UAV-Based Optical Wireless Communication with Multi-Agent Reinforcement Learning. IEEE Transactions on Network and Service Management. 22(2), 1345–1356. DOI: https://doi.org/10.1109/TNSM.2025.3543160
[66] Jain, S., Rajni, Kumar, P., 2026. Intelligent Routing for Smart and Sustainable Transportation: Multi-Modal Real-Time Data Based Deep Reinforcement Learning Framework. In: Sachdeva, S., Watanobe, Y., Bhalla, S. (Eds.). Big Data Analytics in Astronomy, Science, and Engineering. Springer: Cham, Switzerland. pp. 126–137. DOI: https://doi.org/10.1007/978-3-032-23241-0_8
[67] Van Chien, T., Duc, B.T., Tung, N.X., et al., 2026. Single- and Multi-Objective Stochastic Optimization for Next-Generation Networks in the Generative AI and Quantum Computing Era. arXiv preprint. arXiv:2601.02175. DOI: https://doi.org/10.48550/arXiv.2601.02175
[68] Dev, K., Khowaja, S.A., Zeydan, E., et al., 2025. Advanced Architectures Integrated with Agentic AI for Next-Generation Wireless Networks. IEEE Communications Standards Magazine. DOI: https://doi.org/10.1109/MCOMSTD.2025.3632205
[69] Wei, J., Yang, Y., Zhang, X., et al., 2025. From AI for Science to Agentic Science: A Survey on Autonomous Scientific Discovery. arXiv preprint. arXiv:2508.14111. DOI: https://doi.org/10.48550/arXiv.2508.14111
[70] Berger, D., Lye, A., Maidl, A., et al., 2025. Post-Quantum Cryptography for Maritime Systems. In Proceedings of the 2025 IEEE International Conference on Cyber Security and Resilience (CSR), Chania, Greece, 4–6 August 2025; pp. 660–665. DOI: https://doi.org/10.1109/CSR64739.2025.11129991
[71] Wang, Y., Ismail, E.S., 2025. A Review on the Advances, Applications, and Future Prospects of Post-Quantum Cryptography in Blockchain and IoT. IEEE Access. 13, 112962–112977. DOI: https://doi.org/10.1109/ACCESS.2025.3584473
[72] Sharma, V., Banerjee, S., 2019. Analysis of Atmospheric Effects on Satellite-Based Quantum Communication: A Comparative Study. Quantum Information Processing. 18(3), 67. DOI: https://doi.org/10.1007/s11128-019-2182-0