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Turbulent Mixing Simulation via a Quantum Algorithm
- Source :
- AIAA Journal. 56:687-699
- Publication Year :
- 2018
- Publisher :
- American Institute of Aeronautics and Astronautics (AIAA), 2018.
-
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.
- Subjects :
- Physics
Turbulence
Monte Carlo method
Aerospace Engineering
Dirac delta function
Probability density function
01 natural sciences
010305 fluids & plasmas
Physics::Fluid Dynamics
symbols.namesake
0103 physical sciences
symbols
Mathematics::Metric Geometry
Quantum algorithm
Statistical physics
010306 general physics
Reynolds-averaged Navier–Stokes equations
QC
Large eddy simulation
Quantum computer
Subjects
Details
- ISSN :
- 1533385X and 00011452
- Volume :
- 56
- Database :
- OpenAIRE
- Journal :
- AIAA Journal
- Accession number :
- edsair.doi.dedup.....dd31bd78887271655569025730fa35e1