Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

Owen Lockwood

Abstract:

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 github.com/lockwo/rl qvc opt.