We introduce a classical-quantum hybrid approach to computation, allowing for a quadratic performance improvement in the decision process of a learning agent. In particular, a quantum routine is described, which encodes on a quantum register the probability distributions that drive action choices in a reinforcement learning set-up. This routine can be employed by itself in several other contexts where decisions are driven by probabilities. After introducing the algorithm and formally evaluating its performance, in terms of computational complexity and maximum approximation error, we discuss in detail how to exploit it in the Q-learning context.
@article{arxiv.2205.07730,
title = {A hybrid classical-quantum approach to speed-up Q-learning},
author = {A. Sannia and A. Giordano and N. Lo Gullo and C. Mastroianni and F. Plastina},
journal= {arXiv preprint arXiv:2205.07730},
year = {2023}
}