  Variational quantum algorithms (VQAs) are widely applied in the noisy
intermediate-scale quantum era and are expected to demonstrate quantum
advantage. However, training VQAs faces difficulties, one of which is the
so-called barren plateaus (BP) phenomenon, where gradients of cost functions
vanish exponentially with the number of qubits. In this paper, inspired by
transfer learning, where knowledge of pre-solved tasks could be further used in
a different but related work with training efficiency improved, we report a
parameter initialization method to mitigate BP. In the method, a small-sized
task is solved with a VQA. Then the ansatz and its optimum parameters are
transferred to tasks with larger sizes. Numerical simulations show that this
method could mitigate BP and improve training efficiency. A brief discussion on
how this method can work well is also provided. This work provides a reference
for mitigating BP, and therefore, VQAs could be applied to more practical
problems.
