Active Learning in Physics: From 101, to Progress, and Perspective
This is an incremental review paper that synthesizes existing knowledge about Active Learning for researchers in physics and related fields.
This paper provides a comprehensive introduction to Active Learning theory and reviews recent advancements across various domains, while exploring potential integration with quantum machine learning as a synergistic fusion rather than a simple extension.
Active Learning (AL) is a family of machine learning (ML) algorithms that predates the current era of artificial intelligence. Unlike traditional approaches that require labeled samples for training, AL iteratively selects unlabeled samples to be annotated by an expert. This protocol aims to prioritize the most informative samples, leading to improved model performance compared to training with all labeled samples. In recent years, AL has gained increasing attention, particularly in the field of physics. This paper presents a comprehensive and accessible introduction to the theory of AL reviewing the latest advancements across various domains. Additionally, we explore the potential integration of AL with quantum ML, envisioning a synergistic fusion of these two fields rather than viewing AL as a mere extension of classical ML into the quantum realm.