Comparative study of quantum and classical algorithms for renewable energy sources

• Hybrid PV-Bladeless Wind model optimized with 8 classical and 5 quantum algorithms. • PSO converged fastest in 19 iterations, JA reached maximum 7820 W in 81 iterations. • VQE achieved −8.0 energy minima in 125 iterations with NELDER-MEAD optimizer. • QAE and QPMC-QAE predicted power up to ∼7200 W with strong dataset generalization. • Hybrid classical-quantum strategy recommended for scalable renewable optimization. This study compares classical and quantum optimization algorithms for maximizing total power output from a hybrid renewable energy system comprising PV panels and Bl-WT. Classical methods include PSO, SA, CRO, FTMA, CSA, JA, GA, and FL. PSO achieved the fastest convergence at 19 iterations with a peak of 7700 W, while JA reached the highest output of 7820 W in 81 iterations. GA and CSA converged at 99 iterations with 7730 W and 6900 W respectively, FTMA achieved 7750 W in 119 iterations, and SA matched JA’s maximum of 7820 W but required 999 iterations. FL delivered 7250 W without a defined convergence profile. Quantum approaches: VQE, VQD, QAOA, QAE, and QPMC-QAE have shown distinctive capabilities. QAOA with SLSQP converged in 19 iterations to a Hamiltonian minimum of −4.3, while AQGD reached convergence in 3 iterations at −1.0. VQE attained minima near −8.0 with NELDER-MEAD in 125 iterations, and VQD produced excited states with iteration counts from 378 (SLSQP) to 2569 (AQGD). QAE and QPMC-QAE stably predicted power outputs up to ∼7200 W, with QPMC-QAE closely matching actual data and demonstrating scalability across datasets. Results indicate PSO and QAOA as the fastest in classical and quantum domains, JA and SA as delivering the highest classical outputs, and QPMC-QAE as the most reliable quantum estimation method for complex hybrid energy optimization.