论文标题
一种混合经典量子方法，用于加速Q学习
A hybrid classical-quantum approach to speed-up Q-learning
论文作者
论文摘要
我们介绍了一种经典的量子混合方法来计算，从而可以在学习代理的决策过程中进行二次绩效。特别是，描述了一个量子例程，该例程在量子寄存器上编码在增强学习设置中驱动操作选择的概率分布。该例程可以在其他几种情况下由概率驱动的其他几种情况。在引入算法并正式评估其性能（根据计算复杂性和最大近似误差）之后，我们详细讨论了如何在Q学习环境中利用它。
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.
