In this article we introduce an algorithm for mitigating the adverse effects of noise on gradient descent in variational quantum algorithms. This is accomplished by computing a {\emph{regularized}} local classical approximation to the objective function at every gradient descent step. The computational overhead of our algorithm is entirely classical, i.e., the number of circuit evaluations is exactly the same as when carrying out gradient descent using the parameter-shift rules. We empirically demonstrate the advantages offered by our algorithm on randomized parametrized quantum circuits.

翻译：本文提出一种旨在减轻变分量子算法中梯度下降过程噪声负面影响的算法。该算法通过在每次梯度下降步骤中，基于目标函数计算一个正则化的局部经典近似来实现。该算法的计算开销完全属于经典计算范畴，即电路评估次数与使用参数平移规则执行梯度下降时完全相同。我们通过随机参数化量子电路的实验，经验性地展示了该算法带来的优势。