Arunachalam and de Wolf (2018) showed that the sample complexity of quantum batch learning of boolean functions, in the realizable and agnostic settings, has the same form and order as the corresponding classical sample complexities. In this paper, we extend this, ostensibly surprising, message to batch multiclass learning, online boolean learning, and online multiclass learning. For our online learning results, we first consider an adaptive adversary variant of the classical model of Dawid and Tewari (2022). Then, we introduce the first (to the best of our knowledge) model of online learning with quantum examples.

翻译：Arunachalam and de Wolf (2018年) 显示,在可实现和不可知的环境中,对布林功能进行量子批量学习的样本复杂性与相应的古典样本复杂性的形式和顺序相同。 在本文中,我们把这个表面上令人惊讶的信息推广到批量的多级学习、在线布林学习和在线多级学习。对于我们的在线学习结果,我们首先考虑Dawid和Tewari(2022年)的经典模式的适应性对立变体。 然后,我们用量子实例引入了第一个(我们最了解的)在线学习模式。