论文标题
量子科学中机器学习的现代应用
Modern applications of machine learning in quantum sciences
论文作者
论文摘要
在本书中，我们对量子科学中机器学习方法的最新进展提供了全面的介绍。我们涵盖了在监督，无监督和强化学习算法中的深度学习和内核方法的使用，用于阶段分类，多体量子状态的表示，量子反馈控制和量子电路优化。此外，我们介绍并讨论了更专业的主题，例如可区分的编程，生成模型，机器学习的统计方法和量子机器学习。
In this book, we provide a comprehensive introduction to the most recent advances in the application of machine learning methods in quantum sciences. We cover the use of deep learning and kernel methods in supervised, unsupervised, and reinforcement learning algorithms for phase classification, representation of many-body quantum states, quantum feedback control, and quantum circuits optimization. Moreover, we introduce and discuss more specialized topics such as differentiable programming, generative models, statistical approach to machine learning, and quantum machine learning.
