  Variational quantum algorithms (VQAs) are expected to establish valuable
applications on near-term quantum computers. However, recent works have pointed
out that the performance of VQAs greatly relies on the expressibility of the
ansatzes and is seriously limited by optimization issues such as barren
plateaus (i.e., vanishing gradients). This work proposes the state efficient
ansatz (SEA) for accurate ground state preparation with improved trainability.
We show that the SEA can generate an arbitrary pure state with much fewer
parameters than a universal ansatz, making it efficient for tasks like ground
state estimation. Then, we prove that barren plateaus can be efficiently
mitigated by the SEA and the trainability can be further improved most
quadratically by flexibly adjusting the entangling capability of the SEA.
Finally, we investigate a plethora of examples in ground state estimation where
we obtain significant improvements in the magnitude of cost gradient and the
convergence speed.
