Active Learning in Physics: From 101, to Progress, and Perspective

TL;DR

This paper reviews the development and application of Active Learning in physics, highlighting recent advancements, theoretical foundations, and the potential integration with quantum machine learning for future progress.

Contribution

It provides a comprehensive overview of Active Learning in physics, including recent progress and perspectives on combining AL with quantum machine learning.

Findings

Active Learning improves model performance by selecting informative samples.

Recent advancements have expanded AL applications across various physics domains.

Potential for integrating AL with quantum machine learning is explored.

Abstract

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.