Comparative Study of Quantum Algorithms Based on Input Parameters for Renewable Energy Sources

This study compares seven quantum algorithms&#x2014;Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), Quantum Amplitude Estimation (QAE), Quantum Neural Networks (QNN), a novel integration of Quantum Parallel Multi-layer Monte Carlo (QPMC) with QAE proposed in this work, and Quantum-Inspired Evolutionary Optimization (QIEO) to optimize power extraction from photovoltaic (PV) panels and bladeless wind turbines (BI-WT). The analysis considers key parameters, including effective open-circuit voltage (<inline-formula> <tex-math notation="LaTeX">$V_ÓC}$ </tex-math></inline-formula>) of the series-connected PV modules ranging from 85.54V to 89.74V, short-circuit current (<inline-formula> <tex-math notation="LaTeX">$I_{SC}$ </tex-math></inline-formula>) between 2.81A and 21.47A, wind speed (<inline-formula> <tex-math notation="LaTeX">$v$ </tex-math></inline-formula>) spanning from 0.6m/s to 22.3m/s, and solar irradiance (<inline-formula> <tex-math notation="LaTeX">$G_{\theta }$ </tex-math></inline-formula>) varying from 119W/m2 to 924W/m2. VQE and VQD optimize power output effectively, with VQE converging faster. QAOA remains stable but does not always reach optimal power values. QAE estimates power output with high accuracy, aligning with experimental data. QNN achieves lower validation loss when trained for 200 epochs, demonstrating improved generalization. The QPMC with QAE method estimates power output fluctuations with minimal deviation. QIEO exhibits enhanced optimization, with population sizes above 250 individuals leading to stable power output maximization.