{"title":"Clustering by Contour Coreset and Variational Quantum Eigensolver","authors":"Canaan Yung, Muhammad Usman","doi":"10.1002/qute.202300450","DOIUrl":null,"url":null,"abstract":"<p>Recent work has proposed solving the k-means clustering problem on quantum computers via the Quantum Approximate Optimization Algorithm (QAOA) and coreset techniques. Although the current method demonstrates the possibility of quantum k-means clustering, it does not ensure high accuracy and consistency across a wide range of datasets. The existing coreset techniques are designed for classical algorithms, and there is no quantum-tailored coreset technique designed to boost the accuracy of quantum algorithms. This study proposes solving the k-means clustering problem with the variational quantum eigensolver (VQE) and a customized coreset method, the Contour coreset, which is formulated with a specific focus on quantum algorithms. Extensive simulations with synthetic and real-life data demonstrated that the VQE+Contour Coreset approach outperforms existing QAOA+Coreset k-means clustering approaches with higher accuracy and lower standard deviation. This research demonstrates that quantum-tailored coreset techniques can remarkably boost the performance of quantum algorithms compared to generic off-the-shelf coreset techniques.</p>","PeriodicalId":72073,"journal":{"name":"Advanced quantum technologies","volume":"7 8","pages":""},"PeriodicalIF":4.4000,"publicationDate":"2024-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/qute.202300450","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced quantum technologies","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/qute.202300450","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"OPTICS","Score":null,"Total":0}
引用次数: 0
Abstract
Recent work has proposed solving the k-means clustering problem on quantum computers via the Quantum Approximate Optimization Algorithm (QAOA) and coreset techniques. Although the current method demonstrates the possibility of quantum k-means clustering, it does not ensure high accuracy and consistency across a wide range of datasets. The existing coreset techniques are designed for classical algorithms, and there is no quantum-tailored coreset technique designed to boost the accuracy of quantum algorithms. This study proposes solving the k-means clustering problem with the variational quantum eigensolver (VQE) and a customized coreset method, the Contour coreset, which is formulated with a specific focus on quantum algorithms. Extensive simulations with synthetic and real-life data demonstrated that the VQE+Contour Coreset approach outperforms existing QAOA+Coreset k-means clustering approaches with higher accuracy and lower standard deviation. This research demonstrates that quantum-tailored coreset techniques can remarkably boost the performance of quantum algorithms compared to generic off-the-shelf coreset techniques.