  Current structure optimization algorithms optimize the structure of quantum
circuit from scratch for each new task of variational quantum algorithms (VQAs)
without using any prior experience, which is inefficient and time-consuming.
Besides, the number of quantum gates is a hyper-parameter of these algorithms,
which is difficult and time-consuming to determine. In this paper, we propose a
rapid structure optimization algorithm for VQAs which automatically determines
the number of quantum gates and directly generates the optimal structures for
new tasks with the meta-trained graph variational autoencoder (VAE) on a number
of training tasks. We also develop a meta-trained predictor to filter out
circuits with poor performances to further accelerate the algorithm. Simulation
results show that our method output structures with lower loss and it is 70
times faster in running time compared to a state-of-the-art algorithm, namely
DQAS.
