We study optimization problems in health, energy, climate, and artificial intelligence, and introduce a continuous variable variant of Quantum Approximate Optimization Algorithm (QAOA) inspired by quantum Hamiltonian methods. The approach targets problems with real valued variables and is designed to be lightweight and mathematically grounded, with theoretical guarantees on certain instances.