Compact State Space Representation for Quantum Deep Reinforcement Learning with the Help of Auto Encoders

While currently no large and capable quantum computers are available, we would like to utilise the power of smaller quantum devices as efficiently as possible. Current research is limiting itself on small state spaces. For my bachelor thesis I will study a compact state space representation with a variational autoencoder to use in a simple robot-arm environment, to proof that larger state spaces can be compressed and used in quantum machine learning today.