Retrieving Quantum Information with Active Learning

被引：15

作者：

Ding, Yongcheng ^{[1
,2
,3
]}

Martin-Guerrero, Jose D. ^{[4
]}

Sanz, Mikel ^{[3
]}

Magdalena-Benedicto, Rafael ^{[4
]}

Chen, Xi ^{[1
,2
,3
]}

Solano, Enrique ^{[1
,2
,3
,5
,6
]}

机构：

[1] Shanghai Univ, Int Ctr Quantum Artificial Intelligence Sci & Tec, Shanghai 200444, Peoples R China

[2] Shanghai Univ, Dept Phys, Shanghai 200444, Peoples R China

[3] Univ Basque Country, Dept Phys Chem, UPV EHU, Apartado 644, Bilbao 48080, Spain

[4] Univ Valencia, Elect Engn Dept, IDAL, Avinguda Univ S-N, E-46100 Valencia, Spain

[5] Ikerbasque, Basque Fdn Sci, Maria Diaz de Haro 3, Bilbao 48013, Spain

[6] IQM, Munich, Germany

来源：

PHYSICAL REVIEW LETTERS | 2020年 / 124卷 / 14期

基金：

中国国家自然科学基金;

关键词：

ALGORITHM;

D O I：

10.1103/PhysRevLett.124.140504

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.

引用

页数：6