Artificial Intelligence (AI) has the potential to significantly advance
scientific discovery by finding solutions beyond human capabilities. However,
these super-human solutions are often unintuitive and require considerable
effort to uncover underlying principles, if possible at all. Here, we show how
a code-generating language model trained on synthetic data can not only find
solutions to specific problems but can create meta-solutions, which solve an
entire class of problems in one shot and simultaneously offer insight into the
underlying design principles. Specifically, for the design of new quantum
physics experiments, our sequence-to-sequence transformer architecture
generates interpretable Python code that describes experimental blueprints for
a whole class of quantum systems. We discover general and previously unknown
design rules for infinitely large classes of quantum states. The ability to
automatically generate generalized patterns in readable computer code is a
crucial step toward machines that help discover new scientific understanding --
one of the central aims of physics.

人工智能的代码生成语言模型在量子物理实验领域发现了新的设计规则，为机器辅助科学理解提供了重要的工具。