[DEMO] Variational Quantum Circuits for Deep Reinforcement Learning

[ycchen1989]

General information

Name
Samuel Yen-Chi Chen

Affiliation
Brookhaven National Laboratory, Upton NY

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Demo information

Title
Variational Quantum Circuits for Deep Reinforcement Learning

Abstract
This work explores variational quantum circuits for deep reinforcement learning. Specifically, we reshape classical deep reinforcement learning algorithms like experience replay and target network into a representation of variational quantum circuits. Moreover, we use a quantum information encoding scheme to reduce the number of model parameters compared to classical neural networks. To the best of our knowledge, this work is the first proof-of-principle demonstration of variational quantum circuits to approximate the deep Q-value function for decision-making and policy-selection reinforcement learning with experience replay and target network. Besides, our variational quantum circuits can be deployed in many near-term NISQ machines.

Relevant links
We provide a GitHub repo for future studies. The paper has been accepted by IEEE Access and can be downloaded here.

@article{chen19,
title={Variational quantum circuits for deep reinforcement learning},
  author={Chen, Samuel Yen-Chi and Yang, Chao-Han Huck and Qi, Jun and Chen, Pin-Yu and Ma, Xiaoli and Goan, Hsi-Sheng},
  journal={IEEE Access},
  year={2020},
  volume={8},
  pages={141007-141024},
  publisher={IEEE}
}

[josh146]

Thanks @ycchen1989 for this great contribution!

A member of the team will get back to you sometime during this week for a demo review 🙂

[trbromley]

Thanks @ycchen1989 for the contribution! Your demo is now visible on the community demos page! 💯 🚀