Quantum Machine Learning (QML) is considered to be one of the most promising applications of near term quantum devices. However, the optimization of quantum machine learning models presents numerous challenges arising from the imperfections of hardware and the fundamental obstacles in navigating an exponentially scaling Hilbert space. In this work, we evaluate the potential of contemporary methods in deep reinforcement learning to augment gradient based optimization routines in quantum variational circuits. We find that reinforcement learning augmented optimizers consistently outperform gradient descent in noisy environments. All code and pretrained weights are available to replicate the results or deploy the models at https://github.com/lockwo/rl_qvc_opt.

翻译：量子机器学习(QML)被认为是近期量子装置最有希望的应用之一,然而,量子机器学习模型的优化提出了因硬件不完善和在飞速扩展希尔伯特空间时遇到的基本障碍而产生的众多挑战。在这项工作中,我们评估了当前深层强化学习方法的潜力,以扩大量子变换电路中基于梯度的优化常规。我们发现,强化学习可增强在噪音环境中的优化,始终优于梯度下降。所有代码和预先培训的重量都可以在https://github.com/lockwo/rl_qvc_opt上复制结果或部署模型。