Variational quantum algorithms (VQAs) are among the most promising algorithms
in the era of Noisy Intermediate Scale Quantum Devices. Such algorithms are
constructed using a parameterization U($\pmb{\theta}$) with a classical
optimizer that updates the parameters $\pmb{\theta}$ in order to minimize a
cost function $C$. For this task, in general the gradient descent method, or
one of its variants, is used. This is a method where the circuit parameters are
updated iteratively using the cost function gradient. However, several works in
the literature have shown that this method suffers from a phenomenon known as
the Barren Plateaus (BP). In this work, we propose a new method to mitigate
BPs. In general, the parameters $\pmb{\theta}$ used in the parameterization $U$
are randomly generated. In our method they are obtained from a classical neural
network (CNN). We show that this method, besides to being able to mitigate BPs
during startup, is also able to mitigate the effect of BPs during the VQA
training. In addition, we also show how this method behaves for different CNN
architectures.

本文提出了一种使用经典神经网络来生成量子电路参数的方法，以缓解 Barren Plateaus 现象，该方法不仅能够在初始阶段减轻 Barren Plateaus 的影响，还能够在 VQA 训练期间减轻该影响，并展示了该方法在不同 CNN 架构下的表现。