We study a recommender system for quantum data using the linear contextual bandit framework. In each round, a learner receives an observable (the context) and has to recommend from a finite set of unknown quantum states (the actions) which one to measure. The learner has the goal of maximizing the reward in each round, that is the outcome of the measurement on the unknown state. Using this model we formulate the low energy quantum state recommendation problem where the context is a Hamiltonian and the goal is to recommend the state with the lowest energy. For this task, we study two families of contexts: the Ising model and a generalized cluster model. We observe that if we interpret the actions as different phases of the models then the recommendation is done by classifying the correct phase of the given Hamiltonian and the strategy can be interpreted as an online quantum phase classifier.

翻译：我们采用线性上下文赌博机框架研究量子数据的推荐系统。每轮学习中，学习者接收一个可观测量（上下文），需从有限个未知量子态（动作）中推荐待测量的状态。学习者的目标是最大化每轮奖励，即对未知态进行测量的结果。基于该模型，我们提出低能量子态推荐问题：上下文是哈密顿量，目标是推荐能量最低的量子态。针对此任务，我们研究两类上下文：伊辛模型与广义团簇模型。若将动作解释为模型的不同量子相，则推荐过程实质是对给定哈密顿量的正确相进行分类，该策略可视为在线量子相分类器。