The design of new devices and experiments in science and engineering has
historically relied on the intuitions of human experts. This credo, however,
has changed. In many disciplines, computer-inspired design processes, also
known as inverse-design, have augmented the capability of scientists. Here we
visit different fields of physics in which computer-inspired designs are
applied. We will meet vastly diverse computational approaches based on
topological optimization, evolutionary strategies, deep learning, reinforcement
learning or automated reasoning. Then we draw our attention specifically on
quantum physics. In the quest for designing new quantum experiments, we face
two challenges: First, quantum phenomena are unintuitive. Second, the number of
possible configurations of quantum experiments explodes combinatorially. To
overcome these challenges, physicists began to use algorithms for
computer-designed quantum experiments. We focus on the most mature and
\textit{practical} approaches that scientists used to find new complex quantum
experiments, which experimentalists subsequently have realized in the
laboratories. The underlying idea is a highly-efficient topological search,
which allows for scientific interpretability. In that way, some of the
computer-designs have led to the discovery of new scientific concepts and ideas
-- demonstrating how computer algorithm can genuinely contribute to science by
providing unexpected inspirations. We discuss several extensions and
alternatives based on optimization and machine learning techniques, with the
potential of accelerating the discovery of practical computer-inspired
experiments or concepts in the future. Finally, we discuss what we can learn
from the different approaches in the fields of physics, and raise several
fascinating possibilities for future research.

本文介绍了应用于物理学领域的计算机辅助设计方法，着重阐述一种高效拓扑搜索方法用于设计新的复杂量子实验，并通过优化和机器学习技术提出了加速未来实验或理念发现的可能性。