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

Author:

Ding Yongcheng12ORCID,Martín‐Guerrero José D.34,Vives‐Gilabert Yolanda3,Chen Xi15

Affiliation:

1. Department of Physical Chemistry University of the Basque Country UPV/EHU Apartado 644 Bilbao 48080 Spain

2. International Center of Quantum Artificial Intelligence for Science and Technology (QuArtist) and Department of Physics Shanghai University Shanghai 200444 P. R. China

3. Intelligent Data Analysis Laboratory (IDAL) Department of Electronic Engineering ETSE‐UV Universitat de València Avinguda de la Universitat, s/n 46100 Burjassot Valencia 46100 Spain

4. Valencian Graduate School and Research Network of Artificial Intelligence (ValgrAI) Valencia 46022 Spain

5. EHU Quantum Center University of the Basque Country UPV/EHU Barrio Sarriena, s/n 48940 Leioa Biscay 48940 Spain

Abstract

AbstractActive 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, the potential integration of AL is explored 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.

Funder

National Natural Science Foundation of China

Science and Technology Commission of Shanghai Municipality

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computational Theory and Mathematics,Condensed Matter Physics,Mathematical Physics,Nuclear and High Energy Physics,Electronic, Optical and Magnetic Materials,Statistical and Nonlinear Physics

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