  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.
