Author(s)

Wei Hu^1*, James Hu²

¹Department of Computer Science, Houghton College, Houghton, NY, USA.
²Department of Computer and Information Science, University of Pennsylvania, Philadelphia, PA, USA.

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.

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

Hu, W. and Hu, J. (2019) Reinforcement Learning with Deep Quantum Neural Networks. Journal of Quantum Information Science, 9, 1-14. doi: 10.4236/jqis.2019.91001.