Ocean Energy Converters

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• Published Subscription Original Research 24 Apr 2026

  Environmental Impact Assessment of Ocean Energy Converters Using Quantum Machine Learning

  By Palisetti Jhnana Prasuna Alekhya, Manas Kumar Yogi

  Abstract: The accelerating deployment of ocean energy converters (OECs) across tidal, wave, osmotic, and thermal domains necessitates rigorous, data-intensive environmental impact assessment (EIA) frameworks capable of modelling multi-stressor marine ecosystems in real time. Classical machine learning approaches, while operationally mature, encounter scalability bottlenecks and feature correlation limitations when applied to the high-dimensional, non-linear datasets characteristic of offshore monitoring networks. This paper presents a comprehensive quantum machine learning (QML) framework for the …

  Published in Journal of Energy, Environment & Carbon Credits · Vol. 16, Issue 1, 2026 · pp. 22–31 Read article →