论文标题

量子数据学习的通用算法

Universal algorithms for quantum data learning

论文作者

Fanizza, Marco, Skotiniotis, Michalis, Calsamiglia, John, Muñoz-Tapia, Ramon, Sentís, Gael

论文摘要

操作量子传感器和量子计算机将以可用于纯量子处理的量子状态的形式制作数据,为研究物理过程和认证量子技术开辟了新的途径。从这个角度来看,我们回顾了一系列处理测量的工作,这些测量揭示了以产品状态形式给出的量子数据集的结构性特性。这些算法是通用的,这意味着它们的性能不取决于提供数据集的参考框架。需要通用性属性意味着通过小组表示理论来表征最佳测量。

Operating quantum sensors and quantum computers would make data in the form of quantum states available for purely quantum processing, opening new avenues for studying physical processes and certifying quantum technologies. In this Perspective, we review a line of works dealing with measurements that reveal structural properties of quantum datasets given in the form of product states. These algorithms are universal, meaning that their performances do not depend on the reference frame in which the dataset is provided. Requiring the universality property implies a characterization of optimal measurements via group representation theory.

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