Quantum Bandits
This work addresses the bandit problem for quantum computing researchers, offering a novel quantum approach with proven speedup.
The authors tackled the best arm identification problem in a quantum setting by proposing a quantum modeling and an algorithm based on quantum amplitude amplification, achieving a quadratic speedup over classical methods.
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.