Turbulent mixing simulation via a quantum algorithm

Xu, Guanglei and Daley, Andrew J. and Givi, Peyman and Somma, Rolando D. (2018) Turbulent mixing simulation via a quantum algorithm. AIAA Journal, 56 (2). pp. 687-699. ISSN 0001-1452 (https://doi.org/10.2514/1.J055896)

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Abstract

Probability density function (PDF) methods have been very useful in describing many physical aspects of turbulent mixing. In applications of these methods, modeled PDF transport equations are commonly simulated via classical Monte Carlo techniques, which provide estimates of moments of the PDF at arbitrary accuracy. In this work, recently developed techniques in quantum computing and quantum enhanced measurements (quantum metrology) are used to construct a quantum algorithm that accelerates the computation of such estimates. This quantum algorithm provides a quadratic speedup over classical Monte Carlo methods in terms of the number of repetitions needed to achieve the desired precision. This paper illustrates the power of this algorithm by considering a binary scalar mixing process modeled by means of the coalescence/dispersion (C/D) closure. The equation is first simulated using classical Monte Carlo methods, where error estimates for the computation of central moments are provided. Then the quantum algorithm for this problem is simulated by sampling from the same probability distribution as that of the output of a quantum computer, and it is shown that significantly fewer resources are required to achieve the same precision. The results demonstrate potential applications of future quantum computers for simulation of turbulent mixing, and large classes of related problems.

ORCID iDs

Daley, Andrew J. ORCID: https://orcid.org/0000-0001-9005-7761

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• Item metadata

  Item type:	Article
  ID code:	63506
  Dates:	Date Event 28 February 2018 Published 9 November 2017 Published Online 21 September 2017 Accepted
  Subjects:	Science > Physics
  Department:	Faculty of Science > Physics
  Depositing user:	Pure Administrator
  Date deposited:	20 Mar 2018 14:22
  Last modified:	11 Nov 2024 11:57
  Related URLs:	Scopus publication
  URI:	https://strathprints.strath.ac.uk/id/eprint/63506