# Active Learning for the Optimal Design of Multinomial Classification in Physics

arXiv: https://arxiv.org/abs/2109.08612 · HTML: https://mlsanity.com/paper/2109.08612/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 73 references that are in the map.
- First posted on arXiv 17 Sep 2021.

## Builds on (most cited first, 14 of 14)

- [Discovering physical concepts with neural networks](https://mlsanity.com/paper/1807.10300/) (2018)
- [Less is more: sampling chemical space with active learning](https://mlsanity.com/paper/1801.09319/) (2018)
- [Universal Quantum Control through Deep Reinforcement Learning](https://mlsanity.com/paper/1803.01857/) (2018)
- [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)
- [Computer-inspired Quantum Experiments](https://mlsanity.com/paper/2002.09970/) (2020)
- [Deep Reinforcement Learning Control of Quantum Cartpoles](https://mlsanity.com/paper/1910.09200/) (2019)
- [Multiqubit and multilevel quantum reinforcement learning with quantum technologies](https://mlsanity.com/paper/1709.07848/) (2017)
- [Efficiently measuring a quantum device using machine learning](https://mlsanity.com/paper/1810.10042/) (2018)
- [Measurement-based adaptation protocol with quantum reinforcement learning](https://mlsanity.com/paper/1803.05340/) (2018)
- [Noise-Robust End-to-End Quantum Control using Deep Autoregressive Policy Networks](https://mlsanity.com/paper/2012.06701/) (2020)
- [Approximation of quantum control correction scheme using deep neural networks](https://mlsanity.com/paper/1803.05193/) (2018)
- [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)

## Built on by (most cited first, 1 of 1)

- [Active Learning in Physics: From 101, to Progress, and Perspective](https://mlsanity.com/paper/2307.03899/) (2023)

## Abstract (arXiv)

Optimal design for model training is a critical topic in machine learning. Active Learning aims at obtaining improved models by querying samples with maximum uncertainty according to the estimation model for artificially labeling; this has the additional advantage of achieving successful performances with a reduced number of labeled samples. We analyze its capability as an assistant for the design of experiments, extracting maximum information for learning with the minimal cost in fidelity loss, or reducing total operation costs of labeling in the laboratory. We present two typical applications as quantum information retrieval in qutrits and phase boundary prediction in many-body physics. For an equivalent multinomial classification problem, we achieve the correct rate of 99% with less than 2% samples labeled. We reckon that active-learning-inspired physics experiments will remarkably save budget without loss of accuracy.