  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.
