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

arXiv: https://arxiv.org/abs/2307.03899 · HTML: https://mlsanity.com/paper/2307.03899/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 8 Jul 2023.

## Builds on (most cited first, 9 of 9)

- [Quantum Machine Learning](https://mlsanity.com/paper/1611.09347/) (2016)
- [Active learning machine learns to create new quantum experiments](https://mlsanity.com/paper/1706.00868/) (2017)
- [Active Learning of Uniformly Accurate Inter-atomic Potentials for Materials Simulation](https://mlsanity.com/paper/1810.11890/) (2018)
- [A survey of active learning algorithms for supervised remote sensing image classification](https://mlsanity.com/paper/2104.07784/) (2021)
- [Constraining the Parameters of High-Dimensional Models with Active Learning](https://mlsanity.com/paper/1905.08628/) (2019)
- [A quantum active learning algorithm for sampling against adversarial attacks](https://mlsanity.com/paper/1912.03283/) (2019)
- [Active Learning Approach to Optimization of Experimental Control](https://mlsanity.com/paper/2003.11804/) (2020)
- [Active Learning for the Optimal Design of Multinomial Classification in Physics](https://mlsanity.com/paper/2109.08612/) (2021)
- [Active Learning on a Programmable Photonic Quantum Processor](https://mlsanity.com/paper/2208.02104/) (2022)

## Built on by (most cited first, 2 of 2)

- [Direct Acquisition Optimization for Low-Budget Active Learning](https://mlsanity.com/paper/2402.06045/) (2024)
- [Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement](https://mlsanity.com/paper/2504.16136/) (2025)

## Abstract (arXiv)

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.