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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Hybrid quantum algorithms for flow problems.

Sachin S Bharadwaj1, Katepalli R Sreenivasan1,2,3,4

  • 1Department of Mechanical and Aerospace Engineering, New York University, New York, NY 11201.

Proceedings of the National Academy of Sciences of the United States of America
|December 1, 2023
PubMed
Summary
This summary is machine-generated.

We introduce QFlowS, a quantum flow simulator for fluid dynamics. This quantum computing approach enables high-precision simulations of fluid flows, paving the way for practical quantum applications.

Keywords:
computational fluid mechanics (CFD)fluid dynamicsquantum computationquantum linear systems algorithms (QLSA)

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Area of Science:

  • Computational fluid dynamics
  • Quantum computing

Background:

  • Quantum computing (QC) requires practical applications for widespread adoption.
  • Fluid dynamics simulations present a significant computational challenge.

Purpose of the Study:

  • To develop and demonstrate a quantum simulator, QFlowS, for fluid flow simulations.
  • To implement a hybrid, high-precision Quantum Linear Systems Algorithm (QLSA) at the gate level.

Main Methods:

  • Simulated two well-known fluid flows using QFlowS.
  • Implemented a full gate-level hybrid Quantum Linear Systems Algorithm (QLSA).
  • Developed speedup-preserving algorithms for quantum state preparation and in situ postprocessing.

Main Results:

  • Demonstrated QLSA for low Reynolds number fluid flow simulations.
  • Extracted error estimates and power law scaling relating simulation parameters.
  • Showcased end-to-end complexity for computing viscous dissipation rate.

Conclusions:

  • QFlowS offers a viable path toward quantum simulation of fluid dynamics.
  • Highlights the necessity of gate-level implementation considerations in QC.
  • Advances the practical application of quantum computing in scientific domains.