Synopsis
As the integration of renewable energy sources into electrical grids accelerates, challenges related to forecasting, optimization, and grid management arising from the intermittent and variable nature of these sources have become increasingly critical. Quantum computing and Quantum Machine Learning (QML) are emerging as a next-generation paradigm with the potential to surpass classical methods in addressing these challenges. This study systematically reviews both international and national literature across a broad spectrum — from solar and wind energy forecasting to smart grid optimization, energy storage management, and hybrid quantum-classical control architectures — evaluating QML's applications in these domains, existing algorithmic approaches (Variational Quantum Circuits - VQC, Quantum Neural Networks - QNN, QAOA), and research trends specific to Turkey. Findings demonstrate that VQC-based reinforcement learning achieves a 13.6% reduction in energy consumption, while hybrid quantum algorithms can enhance smart grid efficiency by up to 25%. In Turkey, pioneering QNN studies on wind power forecasting conducted through the collaboration of Yıldız Technical University and Bolu Abant İzzet Baysal University represent the leading national contributions, while the QuanT platform developed through the TOBB ETU-Aselsan partnership strengthens Turkey's strategic position in the quantum technologies ecosystem. The study identifies existing research gaps and presents recommendations regarding Turkey's unique positioning at the intersection of renewable energy and quantum technologies.
