  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.
