  Multi-armed bandits are one of the theoretical pillars of reinforcement
learning. Recently, the investigation of quantum algorithms for multi-armed
bandit problems was started, and it was found that a quadratic speed-up (in
query complexity) is possible when the arms and the randomness of the rewards
of the arms can be queried in superposition. Here we introduce further bandit
models where we only have limited access to the randomness of the rewards, but
we can still query the arms in superposition. We show that then the query
complexity is the same as for classical algorithms. This generalizes the prior
result that no speed-up is possible for unstructured search when the oracle has
positive failure probability.

Quantum Physics;Machine Learning