用语言模型进行量子实验的元设计
Meta-Designing Quantum Experiments with Language Models
AI总结：
本文提出通过语言模型进行量子实验的元设计，生成可读代码解决复杂问题，扩展到更大实验并应用于凝聚态物理等领域。
摘要：
人工智能（AI）可以解决超越人类能力的复杂科学问题，但由此产生的解决方案对底层物理原理提供很少的见解。一个显著的例子是量子物理，其中计算机可以发现生成特定量子态的实验，但不清楚如何自动寻找通用设计概念。在这里，我们通过训练一个基于Transformer的语言模型来创建可读的Python代码，该代码在单次遍历中解决整个问题类。这种策略，我们称之为元设计，使科学家能够在不进行额外优化的情况下获得更深入的理解并扩展到更大的实验。为了展示我们方法的有效性，我们揭示了重要量子态的之前未知的实验泛化，例如从凝聚态物理中。元设计的基本方法可以自然地扩展到材料科学或工程等领域。
英文摘要：
Artificial Intelligence (AI) can solve complex scientific problems beyond human capabilities, but the resulting solutions offer little insight into the underlying physical principles. One prominent example is quantum physics, where computers can discover experiments for the generation of specific quantum states, but it is unclear how finding general design concepts can be automated. Here, we address this challenge by training a transformer-based language model to create human-readable Python code, which solves an entire class of problems in a single pass. This strategy, which we call meta-design, enables scientists to gain a deeper understanding and extrapolate to larger experiments without additional optimization. To demonstrate the effectiveness of our approach, we uncover previously unknown experimental generalizations of important quantum states, e.g. from condensed matter physics. The underlying methodology of meta-design can naturally be extended to fields such as materials science or engineering.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
