Active Learning on a Programmable Photonic Quantum Processor

TL;DR

This paper demonstrates that active learning significantly reduces labeling and computational costs in quantum machine learning by implementing and testing AL-enhanced quantum classifiers on a programmable photonic quantum processor.

Contribution

The study designs and implements active learning-enabled variational quantum classifiers on a programmable photonic quantum processor, showing substantial cost reductions in training.

Findings

AL saves up to 85% of labeling efforts

AL reduces computational efforts by 91.6%

AL enhances quantum classifier efficiency

Abstract

Training a quantum machine learning model generally requires a large labeled dataset, which incurs high labeling and computational costs. To reduce such costs, a selective training strategy, called active learning (AL), chooses only a subset of the original dataset to learn while maintaining the trained model's performance. Here, we design and implement two AL-enpowered variational quantum classifiers, to investigate the potential applications and effectiveness of AL in quantum machine learning. Firstly, we build a programmable free-space photonic quantum processor, which enables the programmed implementation of various hybrid quantum-classical computing algorithms. Then, we code the designed variational quantum classifier with AL into the quantum processor, and execute comparative tests for the classifiers with and without the AL strategy. The results validate the great advantage of AL in quantum machine learning, as it saves at most $85\%$ labeling efforts and $91.6\%$ percent computational efforts compared to the training without AL on a data classification task. Our results inspire AL's further applications in large-scale quantum machine learning to drastically reduce training data and speed up training, underpinning the exploration of practical quantum advantages in quantum physics or real-world applications.