标题： 一种用于设计量子实验的混合机器学习算法 显示英文标题

标题： A hybrid machine-learning algorithm for designing quantum experiments

摘要： 我们引入了一种混合机器学习算法，用于设计产生特定量子态的量子光学实验。 我们的算法成功找到了我们要求的全部5种状态的实验方案，包括 薛定谔猫态和三次相位态，所有状态的保真度都超过 $96\%$。 在这里，我们特别关注设计现实的实验，因此算法的所有设计仅包含当前技术可用的实验元件。 我们算法的核心是一种遗传算法，用于寻找实验元件的最佳排列，但为了加快初始搜索，我们结合了一个神经网络来分类量子态。 后者本身也有独立的兴趣，因为它能够快速学习根据光子数分布准确分类量子态。 显示英文摘要

摘要： We introduce a hybrid machine-learning algorithm for designing quantum optics experiments that produce specific quantum states. Our algorithm successfully found experimental schemes to produce all 5 states we asked it to, including Schr\"odinger cat states and cubic phase states, all to a fidelity of over $96\%$. Here we specifically focus on designing realistic experiments, and hence all of the algorithm's designs only contain experimental elements that are available with current technology. The core of our algorithm is a genetic algorithm that searches for optimal arrangements of the experimental elements, but to speed up the initial search we incorporate a neural network that classifies quantum states. The latter is of independent interest, as it quickly learned to accurately classify quantum states given their photon-number distributions.