Work

Retrieving Quantum Information with Active Learning

Year: 2020

Type: article

Abstract: 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.

Source: Physical Review Letters

DOI: 10.1103/physrevlett.124.140504

Authors: Yongcheng Ding, José D. Martín-Guerrero, Mikel Sanz, Rafael Magdalena-Benedicto, Xi Chen, Enrique Solano