Active Learning for the Optimal Design of Multinomial Classification in Physics
This work addresses budget constraints in physics research by enabling accurate experiments with minimal labeled data, though it is incremental as it applies an existing method to new domains.
The paper tackles the problem of reducing labeling costs in physics experiments by applying active learning to multinomial classification, achieving 99% accuracy with less than 2% of samples labeled in tasks like quantum information retrieval and phase boundary prediction.
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.