Universal algorithms for quantum data learning

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 measure...

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Bibliographic Details
Published inEurophysics letters Vol. 140; no. 2; pp. 28001 - 28007
Main Authors Fanizza, Marco, Skotiniotis, Michalis, Calsamiglia, John, Muñoz-Tapia, Ramon, Sentís, Gael
Format Journal Article
LanguageEnglish
Published Les Ulis EDP Sciences, IOP Publishing and Società Italiana di Fisica 01.10.2022
IOP Publishing
Subjects
Algorithms
Datasets
Machine learning
Quantum computers
Quantum sensors
Structural analysis
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Summary: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.
ISSN:0295-5075
1286-4854
DOI:10.1209/0295-5075/ac9c29