These brief lecture notes cover the basics of neural networks and deep learning as well as their applications in the quantum domain, for physicists without prior knowledge. In the first part, we describe training using backpropagation, image classification, convolutional networks and autoencoders. The second part is about advanced techniques like reinforcement learning (for discovering control strategies), recurrent neural networks (for analyzing time traces), and Boltzmann machines (for learning probability distributions). In the third lecture, we discuss first recent applications to quantum physics, with an emphasis on quantum information processing machines. Finally, the fourth lecture is devoted to the promise of using quantum effects to accelerate machine learning.

中文翻译：

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机器学习和量子设备

这些简短的讲义涵盖了神经网络和深度学习的基础知识以及它们在量子领域的应用，适用于没有先验知识的物理学家。在第一部分，我们描述了使用反向传播、图像分类、卷积网络和自动编码器的训练。第二部分是关于强化学习（用于发现控制策略）、循环神经网络（用于分析时间轨迹）和玻尔兹曼机（用于学习概率分布）等高级技术。在第三讲中，我们讨论了量子物理学的最新应用，重点是量子信息处理机器。最后，第四讲致力于利用量子效应加速机器学习的前景。