At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and applications for QML. We highlight differences between quantum and classical machine learning, with a focus on quantum neural networks and quantum deep learning. Finally, we discuss opportunities for quantum advantage with QML.

翻译：在机器学习与量子计算的交叉领域，量子机器学习（QML）有望加速数据分析，特别是针对量子数据的分析，在量子材料、生物化学和高能物理等领域具有应用前景。然而，QML模型的可训练性仍面临挑战。本文回顾了QML的现有方法和应用，重点分析了量子机器学习与经典机器学习之间的差异，尤其关注量子神经网络与量子深度学习。最后，我们探讨了利用QML实现量子优势的机遇。