Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

Understand

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.