Quantum Algorithms’ Approach: A Case Study on Bladeless Wind Turbine and Solar Panel

The researched data shows that quantum algorithms have been mostly used for battery’s chemistry and conventional grid systems. These algorithms have proved to be computationally cheaper as a common meritorious aspect in almost all implementations. However, usage of quantum algorithms to integrate two renewable energy systems is yet to be explored. This study investigates the application of quantum algorithms—Variational Quantum Eigensolver (VQE), Variational Quantum Deflation (VQD), Quantum Approximate Optimization Algorithm (QAOA), and Quantum Amplitude Estimation (QAE)—to optimize energy extraction from solar panels and bladeless wind turbines. Nine different optimizers are used on 3066 and 11547 datapoints of wind and solar respectively, to comment about optimality between QAOA, VQD and VQE. Results demonstrate that optimizers like NELDER_MEAD and COBYLA excel in convergence speed, whereas in case of energy minimization SPSA and SLSQP for VQE, COBYLA and P_BFGS for VQD have outperformed other optimizers. QAOA effectively tackles combinatorial optimization tasks as convergence speed for all optimizers has been relatively rapid. Since VQE, VQD and QAOA have not been accurate in finding optimal data point, hence QAE is utilized. Additionally, QAE’s precision in amplifying high-probability quantum states aligns well with the periodic and structured nature of wind and solar energy datasets.