| Issue |
EPL
Volume 140, Number 2, October 2022
|
|
|---|---|---|
| Article Number | 28001 | |
| Number of page(s) | 7 | |
| Section | Quantum information | |
| DOI | https://doi.org/10.1209/0295-5075/ac9c29 | |
| Published online | 07 November 2022 | |
Universal algorithms for quantum data learning
Física Teòrica: Informació i Fenòmens Quàntics, Departament de Física, Universitat Autònoma de Barcelona 08193 Bellatera (Barcelona), Spain
(a) E-mail: gael.sentis@uab.cat (corresponding author)
Received: 2 August 2022
Accepted: 20 October 2022
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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