Quantum walks (QWs) have a property that classical random walks (RWs) do not possess -- the coexistence of linear spreading and localization -- and this property is utilized to implement various kinds of applications. This paper proposes RW- and QW-based algorithms for multi-armed-bandit (MAB) problems. We show that, under some settings, the QW-based model realizes higher performance than the corresponding RW-based one by associating the two operations that make MAB problems difficult -- exploration and exploitation -- with these two behaviors of QWs.

翻译：量子游走具有经典随机游走不具备的特性——线性扩散与局域化的共存，该特性已被用于实现多种应用。本文提出基于随机游走与量子游走的多臂赌博机问题算法。研究表明，在特定设置下，通过将导致多臂赌博机问题困难的两个操作——探索与利用——与量子游走的两种行为相关联，基于量子游走的模型相比对应的随机游走模型能够实现更高性能。