Mitigating barren plateaus of variational quantum eigensolvers

Understand

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.