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Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., Dieleman, S., Grewe, D., Nham, J., Kalchbrenner, N., Sutskever, I., Lillicrap, T., Leach, M., Kavukcuoglu, K., Graepel, T. and Hassabis, D. (2016) Mastering the Game of Go with Deep Neural Networks and Tree Search. Nature, 529, 484-489.
https://doi.org/10.1038/nature16961

has been cited by the following article:

• TITLE: Reinforcement Learning with Deep Quantum Neural Networks

  AUTHORS: Wei Hu, James Hu

  KEYWORDS: Continuous-Variable Quantum Computers, Quantum Machine Learning, Quantum Reinforcement Learning, Deep Learning, Q Learning, Actor-Critic, Grid World Environment

  JOURNAL NAME: Journal of Quantum Information Science, Vol.9 No.1, March 8, 2019

  ABSTRACT: The advantage of quantum computers over classical computers fuels the recent trend of developing machine learning algorithms on quantum computers, which can potentially lead to breakthroughs and new learning models in this area. The aim of our study is to explore deep quantum reinforcement learning (RL) on photonic quantum computers, which can process information stored in the quantum states of light. These quantum computers can naturally represent continuous variables, making them an ideal platform to create quantum versions of neural networks. Using quantum photonic circuits, we implement Q learning and actor-critic algorithms with multilayer quantum neural networks and test them in the grid world environment. Our experiments show that 1) these quantum algorithms can solve the RL problem and 2) compared to one layer, using three layer quantum networks improves the learning of both algorithms in terms of rewards collected. In summary, our findings suggest that having more layers in deep quantum RL can enhance the learning outcome.