论文标题
量子土匪
Quantum Bandits
论文作者
论文摘要
我们考虑称为{\ em最好的手臂识别}（bai）的匪徒问题的量子版本。我们首先提出了BAI问题的量子建模，该模型假设学习剂和环境都是量子。然后，我们提出了一种基于量子振幅扩增的算法来求解BAI。我们正式在所有问题的情况下正式分析了算法的行为，并且特别表明，它能够比经典情况下的最佳解决方案更快地获得四边形的最佳解决方案。
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.
