VQA have attracted a lot of attention from the quantum computing community
for the last few years. Their hybrid quantum-classical nature with relatively
shallow quantum circuits makes them a promising platform for demonstrating the
capabilities of NISQ devices. Although the classical machine learning community
focuses on gradient-based parameter optimization, finding near-exact gradients
for VQC with the parameter-shift rule introduces a large sampling overhead.
Therefore, gradient-free optimizers have gained popularity in quantum machine
learning circles. Among the most promising candidates is the SPSA algorithm,
due to its low computational cost and inherent noise resilience. We introduce a
novel approach that uses the approximated gradient from SPSA in combination
with state-of-the-art gradient-based classical optimizers. We demonstrate
numerically that this outperforms both standard SPSA and the parameter-shift
rule in terms of convergence rate and absolute error in simple regression
tasks. The improvement of our novel approach over SPSA with stochastic gradient
decent is even amplified when shot- and hardware-noise are taken into account.
We also demonstrate that error mitigation does not significantly affect our
results.

本文介绍了一种结合了近似梯度和最先进的基于梯度的优化算法的新方法，该方法在简单回归任务中的收敛速度和绝对误差方面超越了标准 SPSA 和参数偏移规则，而且在考虑了 shot- 和硬件噪声之后，优于 SPSA 和随机梯度下降方法。