Researchers are exploring new methods for optimizing variational quantum eigensolvers, including gradient-free approaches. Different strategies are being proposed, such as greedy adaptive algorithms, generative flow-based warm starts, and interpolation-based coordinate descent methods. These approaches aim to improve the performance of quantum simulations, particularly for fermionic systems with global interactions.