A quasi-random approach to matrix spectral analysis

Michael Ben-Or, Lior Eldar

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

2 Scopus citations


Inspired by quantum computing algorithms for Linear Algebra problems [6, 14] we study how simulation on a classical computer of this type of “Phase Estimation algorithms” performs when we apply it to the Eigen-Problem of Hermitian matrices. The result is a completely new, efficient and stable, parallel algorithm to compute an approximate spectral decomposition of any Hermitian matrix. The algorithm can be implemented by Boolean circuits in O(log2n) parallel time with a total cost of O(nω+1) Boolean operations. This Boolean complexity matches the best known O(log2n) parallel time algorithms, but unlike those algorithms our algorithm is (logarithmically) stable, so it may lead to actual implementations, allowing fast parallel computation of eigenvectors and eigenvalues in practice. Previous approaches to solve the Eigen-Problem generally use randomization to avoid bad conditions - as we do. Our algorithm makes further use of randomization in a completely new way, taking random powers of a unitary matrix to randomize the phases of its eigenvalues. Proving that a tiny Gaussian perturbation and a random polynomial power are su cient to ensure almost pairwise independence of the phases (mod 2π) is the main technical contribution of this work. It relies on the theory of low-discrepancy or quasi-random sequences - a theory, which to the best of our knowledge, has not been connected thus far to linear algebra problems. Hence, we believe that further study of this new connection will lead to additional improvements.

Original languageAmerican English
Title of host publication9th Innovations in Theoretical Computer Science, ITCS 2018
EditorsAnna R. Karlin
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
ISBN (Electronic)9783959770606
StatePublished - 1 Jan 2018
Event9th Innovations in Theoretical Computer Science, ITCS 2018 - Cambridge, United States
Duration: 11 Jan 201814 Jan 2018

Publication series

NameLeibniz International Proceedings in Informatics, LIPIcs
ISSN (Print)1868-8969


Conference9th Innovations in Theoretical Computer Science, ITCS 2018
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© Michael Ben-Or and Lior Eldar.


  • Eigenvalues
  • Eigenvectors
  • Low-discrepancy sequence


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