Quantum Variational Methods are promising near-term applications of quantum machines, not only because of their potential advantages in solving certain computational tasks and understanding quantum physics but also because of their feasibility on near-term quantum machines. However, many challenges remain in order to unleash the full potential of quantum variational methods, especially in the design of efficient training methods for each domain-specific quantum variational ansatzes. This paper proposes a theory-guided principle in order to tackle the training issue of quantum variational methods and highlights some successful examples.