Randomized method for estimating the von neumann entropy of large-scale density matrices

Hayoung Choi, Xuming Song, Yuanming Shi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

In this article the randomized algorithm for estimating the von Neumann entropy of large-scale density matrices is considered. By capturing the dominant eigenspace via a k-rank approximation of the density matrix we estimate the entropy. We analyze the error bound with the eigenvalues of density matrix. Numerical experiments show that the proposed method is extensively efficient for large-scale density matrices.

Original languageEnglish
Title of host publication2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages296-300
Number of pages5
ISBN (Electronic)9781728112954
DOIs
StatePublished - 2 Jul 2018
Event2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Anaheim, United States
Duration: 26 Nov 201829 Nov 2018

Publication series

Name2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018 - Proceedings

Conference

Conference2018 IEEE Global Conference on Signal and Information Processing, GlobalSIP 2018
Country/TerritoryUnited States
CityAnaheim
Period26/11/1829/11/18

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