Optimizing Quantum Variational Circuits with Deep Reinforcement Learning

2021-09-07 · Owen Lockwood

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.

📄 PDF Abstract BibTeX arXiv:2109.03188

Code (1)

lockwo/rl_qvc_opt 공식 구현 tf

Tasks

BIG-bench Machine LearningDeep Reinforcement LearningQuantum Machine Learning reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

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