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：在机器学习和量子计算交汇处,量子机器学习(QML)有可能加快数据分析,特别是量子数据,并应用量子材料、生物化学和高能物理学,然而,在QML模型的可训练性方面仍然存在挑战。我们在这里审查QML的现有方法和应用程序。我们强调量子和经典机器学习之间的差异,重点是量子神经网络和量子深层学习。最后,我们讨论与QML取得量子优势的机会。</s>