The design of new devices and experiments has historically relied on the intuition of human experts. Now, design inspirations from computers are increasingly augmenting the capability of scientists. We briefly overview different fields of physics that rely on computer-inspired designs using a variety of computational approaches based on topological optimization, evolutionary strategies, deep learning, reinforcement learning or automated reasoning. Then we focus specifically on quantum physics. When designing new quantum experiments, there are two challenges: quantum phenomena are unintuitive, and the number of possible configurations of quantum experiments explodes exponentially. These challenges can be overcome by using computer-designed quantum experiments. We focus on the most mature and practical approaches to find new complex quantum experiments, which have subsequently been realized in the lab. These methods rely on a highly efficient topological search, which can inspire new scientific ideas. We review several extensions and alternatives based on various optimization and machine learning techniques. Finally, we discuss what can be learned from the different approaches and outline several future directions.

历史上，新设备和实验的设计依赖于人类专家的直觉。现在，计算机的设计灵感正日益增强科学家的能力。我们简要概述了基于计算机启发性设计的物理学的不同领域，这些领域使用了基于拓扑优化，进化策略，深度学习，强化学习或自动推理的各种计算方法。然后，我们专门关注量子物理学。在设计新的量子实验时，存在两个挑战：量子现象是不直观的，量子实验的可能配置数量呈指数级爆炸。通过使用计算机设计的量子实验可以克服这些挑战。我们专注于找到新的复杂量子实验的最成熟，最实用的方法，这些新实验随后在实验室中实现。这些方法依赖于高效的拓扑搜索，可以激发新的科学思想。我们将基于各种优化和机器学习技术来回顾几种扩展和替代方案。最后，我们讨论可以从不同的方法中学到什么，并概述几个未来的方向。