Turbulent Mixing Simulation via a Quantum Algorithm

Xu, Guanglei; Daley, Andrew J.; Givi, Peyman; Somma, Rolando D.

doi:10.2514/1.J055896

Title: Turbulent Mixing Simulation via a Quantum Algorithm

Journal Article · Thu Nov 09 00:00:00 EST 2017 · AIAA Journal

DOI:https://doi.org/10.2514/1.J055896· OSTI ID:1458951

Xu, Guanglei ^[1]; Daley, Andrew J. ^[1]; Givi, Peyman ^[2]; Somma, Rolando D. ^[3]

Univ. of Strathclyde, Glasgow, Scotland (United Kingdom). Dept. of Physics. SUPA
Univ. of Pittsburgh, PA (United States). Mechanical Engineering and Petroleum Engineering
Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

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 paper, 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. Finally, the results demonstrate potential applications of future quantum computers for simulation of turbulent mixing, and large classes of related problems.

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Research Organization:: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Sponsoring Organization:: USDOE; US Air Force Office of Scientific Research (AFOSR); Engineering and Physical Sciences Research Council (EPSRC)

Grant/Contract Number:: AC52-06NA25396; FA9550-12-1-0057; EP/K000586/1

OSTI ID:: 1458951

Report Number(s):: LA-UR-16-25360

Journal Information:: AIAA Journal, Vol. 56, Issue 2; ISSN 0001-1452

Publisher:: AIAACopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 12 works

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