← Back to papers
Universal algorithms for quantum data learning
Marco Fanizza, M. Skotiniotis, J. Calsamiglia, R. Muñoz-Tapia, G. Sentís·October 20, 2022·DOI: 10.1209/0295-5075/ac9c29
Physics
AI Breakdown
Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.
Abstract
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.