Retrieving Quantum Information with Active Learning.

Clicks: 272
ID: 105036
2020
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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.
Reference Key
ding2020retrievingphysical Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Ding, Yongcheng;Martín-Guerrero, José D;Sanz, Mikel;Magdalena-Benedicto, Rafael;Chen, Xi;Solano, Enrique;
Journal physical review letters
Year 2020
DOI
10.1103/PhysRevLett.124.140504
URL
Keywords
Deep learning convolutional neural networks ensemble classification database generalization sleep staging

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