  Quantum Machine Learning (QML) is a young but rapidly growing field where
quantum information meets machine learning. Here, we will introduce a new QML
model generalizing the classical concept of Reinforcement Learning to the
quantum domain, i.e. Quantum Reinforcement Learning (QRL). In particular we
apply this idea to the maze problem, where an agent has to learn the optimal
set of actions in order to escape from a maze with the highest success
probability. To perform the strategy optimization, we consider an hybrid
protocol where QRL is combined with classical deep neural networks. In
particular, we find that the agent learns the optimal strategy in both the
classical and quantum regimes, and we also investigate its behaviour in a noisy
environment. It turns out that the quantum speedup does robustly allow the
agent to exploit useful actions also at very short time scales, with key roles
played by the quantum coherence and the external noise. This new framework has
the high potential to be applied to perform different tasks (e.g. high
transmission/processing rates and quantum error correction) in the
new-generation Noisy Intermediate-Scale Quantum (NISQ) devices whose topology
engineering is starting to become a new and crucial control knob for practical
applications in real-world problems. This work is dedicated to the memory of
Peter Wittek.
