TITLE:
Q Learning with Quantum Neural Networks
AUTHORS:
Wei Hu, James Hu
KEYWORDS:
Continuous-Variable Quantum Computers, Quantum Machine Learning, Quantum Reinforcement Learning, Q Learning, Grid World Environment
JOURNAL NAME:
Natural Science,
Vol.11 No.1,
January
24,
2019
ABSTRACT: Applying quantum
computing techniques to machine learning has attracted widespread attention
recently and quantum machine learning has become a hot research topic. There
are three major categories of machine learning: supervised, unsupervised, and
reinforcement learning (RL). However, quantum RL has made the least progress
when compared to the other two areas. In this study, we implement the
well-known RL algorithm Q learning with a quantum neural network and evaluate
it in the grid world environment. RL is learning through interactions with the
environment, with the aim of discovering a strategy to maximize the expected
cumulative rewards. Problems in RL bring in unique challenges to the study with
their sequential nature of learning, potentially long delayed reward signals,
and large or infinite size of state and action spaces. This study extends our
previous work on solving the contextual bandit problem using a quantum neural
network, where the reward signals are immediate after each action.