Optimizing Quantum Variational Circuits with Deep Reinforcement Learning
This work addresses optimization problems for researchers in quantum machine learning, but it is incremental as it builds on existing methods.
The paper tackles the challenge of optimizing quantum machine learning models on noisy quantum hardware by using deep reinforcement learning to augment gradient-based optimization, finding that this approach consistently outperforms gradient descent in such environments.
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.