  Optimal design for model training is a critical topic in machine learning.
Active Learning aims at obtaining improved models by querying samples with
maximum uncertainty according to the estimation model for artificially
labeling; this has the additional advantage of achieving successful
performances with a reduced number of labeled samples. We analyze its
capability as an assistant for the design of experiments, extracting maximum
information for learning with the minimal cost in fidelity loss, or reducing
total operation costs of labeling in the laboratory. We present two typical
applications as quantum information retrieval in qutrits and phase boundary
prediction in many-body physics. For an equivalent multinomial classification
problem, we achieve the correct rate of 99% with less than 2% samples labeled.
We reckon that active-learning-inspired physics experiments will remarkably
save budget without loss of accuracy.
