Quantum Bandits
Abstract
We consider the quantum version of the bandit problem known as {\em best arm identification} (BAI). We first propose a quantum modeling of the BAI problem, which assumes that both the learning agent and the environment are quantum; we then propose an algorithm based on quantum amplitude amplification to solve BAI. We formally analyze the behavior of the algorithm on all instances of the problem and we show, in particular, that it is able to get the optimal solution quadratically faster than what is known to hold in the classical case.
 Publication:

arXiv eprints
 Pub Date:
 February 2020
 arXiv:
 arXiv:2002.06395
 Bibcode:
 2020arXiv200206395C
 Keywords:

 Computer Science  Machine Learning;
 Computer Science  Artificial Intelligence;
 Quantum Physics;
 Statistics  Machine Learning
 EPrint:
 All your comments are very welcome!