Quantum machine learning (QML) requires significant quantum resources to
achieve quantum advantage. Research should prioritize both the efficient design
of quantum architectures and the development of learning strategies to optimize
resource usage. We propose a framework called quantum curriculum learning
(Q-CurL) for quantum data, where the curriculum introduces simpler tasks or
data to the learning model before progressing to more challenging ones. We
define the curriculum criteria based on the data density ratio between tasks to
determine the curriculum order. We also implement a dynamic learning schedule
to emphasize the significance of quantum data in optimizing the loss function.
Empirical evidence shows that Q-CurL enhances the training convergence and the
generalization for unitary learning tasks and improves the robustness of
quantum phase recognition tasks. Our framework provides a general learning
strategy, bringing QML closer to realizing practical advantages.

量子机器学习中，优化资源使用和设计量子架构是重要研究方向。我们提出了量子课程学习 (Q-CurL) 框架，通过引入简单任务和数据依据数据密度比例来确定学习顺序，优化损失函数，提高训练收敛性和泛化性，从而将量子机器学习更接近于实际应用价值。