Demonstration of Topological Data Analysis on a Quantum Processor
Abstract
Topological data analysis offers a robust way to extract useful information from noisy, unstructured data by identifying its underlying structure. Recently, an efficient quantum algorithm was proposed [Lloyd, Garnerone, Zanardi, Nat. Commun. 7, 10138 (2016)] for calculating Betti numbers of data points -- topological features that count the number of topological holes of various dimensions in a scatterplot. Here, we implement a proof-of-principle demonstration of this quantum algorithm by employing a six-photon quantum processor to successfully analyze the topological features of Betti numbers of a network including three data points, providing new insights into data analysis in the era of quantum computing.
Cite
@article{arxiv.1801.06316,
title = {Demonstration of Topological Data Analysis on a Quantum Processor},
author = {He-Liang Huang and Xi-Lin Wang and Peter P. Rohde and Yi-Han Luo and You-Wei Zhao and Chang Liu and Li Li and Nai-Le Liu and Chao-Yang Lu and Jian-Wei Pan},
journal= {arXiv preprint arXiv:1801.06316},
year = {2023}
}
Comments
Typos and minor corrections. For the first time, we have experimentally demonstrated that quantum computing can analyze big data using techniques from topology. Any comments are welcome