Mitigating barren plateaus of variational quantum eigensolvers
This addresses a critical optimization bottleneck for variational quantum algorithms, which are key for near-term quantum computing applications, though it appears incremental as it builds on existing ansatz methods.
The paper tackles the problem of barren plateaus in variational quantum eigensolvers, which hinder optimization, by proposing the state efficient ansatz (SEA) to improve trainability and achieve significant gains in gradient magnitude and convergence speed for ground state estimation.
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.