Retrieving Quantum Information with Active Learning

TL;DR

This paper introduces active learning techniques to improve quantum information retrieval, significantly reducing labeling costs while maintaining high accuracy, thereby advancing quantum experiment data analysis.

Contribution

It applies active learning to quantum information retrieval, demonstrating efficient classification with minimal data labeling and fidelity loss reduction.

Findings

Labeling only 5% of data yields nearly 90% rate estimation.

Active learning reduces data labeling costs in quantum experiments.

Enhanced data analysis methods for quantum technologies.

Abstract

Active learning is a machine learning method aiming at optimal design for model training. At variance with supervised learning, which labels all samples, active learning provides an improved model by labeling samples with maximal uncertainty according to the estimation model. Here, we propose the use of active learning for efficient quantum information retrieval, which is a crucial task in the design of quantum experiments. Meanwhile, when dealing with large data output, we employ active learning for the sake of classification with minimal cost in fidelity loss. Indeed, labeling only 5% samples, we achieve almost 90% rate estimation. The introduction of active learning methods in the data analysis of quantum experiments will enhance applications of quantum technologies.