Quantum machine learning (QML) is a rapidly growing area of research at the intersection of classical machine learning and quantum information theory. One area of considerable interest is the use of QML to learn information contained within quantum states themselves. In this work, we propose a novel approach in which the extraction of information from quantum states is undertaken in a classical representational-space, obtained through the training of a hybrid quantum autoencoder (HQA). Hence, given a set of pure states, this variational QML algorithm learns to identify—and classically represent—their essential distinguishing characteristics, subsequently giving rise to a new paradigm for clustering and semi-supervised classification. The analysis and employment of the HQA model are presented in the context of amplitude encoded states—which in principle can be extended to arbitrary states for the analysis of structure in non-trivial quantum data sets.

量子机器学习 (QML) 是经典机器学习和量子信息理论交叉领域的一个快速发展的研究领域。一个相当有趣的领域是使用 QML 来学习包含在量子态本身中的信息。在这项工作中，我们提出了一种新方法，其中从量子态中提取信息是在经典的表示空间中进行的，通过混合量子自动编码器的训练获得（总部）。因此，给定一组纯状态，这种变分 QML 算法学会识别并经典地表示它们的基本区别特征，随后产生了聚类和半监督分类的新范式。HQA 模型的分析和使用是在幅度编码状态的背景下提出的——原则上可以扩展到任意状态，以分析非平凡量子数据集中的结构。