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Quantum lattice Boltzmann method for simulating nonlinear fluid dynamics

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arxiv 2502.16568 v1 pith:SS6OJQSZ submitted 2025-02-23 physics.flu-dyn nlin.CGphysics.comp-phquant-ph

classification physics.flu-dynnlin.CGphysics.comp-phquant-ph
keywords quantumlatticenonlineardynamicsfluidalgorithmsboltzmannlinear
verification ladder T0 review T1 audit T2 compute T3 formal
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abstract

Quantum computing holds great promise to accelerate scientific computations in fluid dynamics and other classical physical systems. While various quantum algorithms have been proposed for linear flows, developing quantum algorithms for nonlinear problems remains a significant challenge. We introduce a novel node-level ensemble description of lattice gas for simulating nonlinear fluid dynamics on a quantum computer. This approach combines the advantages of the lattice Boltzmann method, which offers low-dimensional representation, and lattice gas cellular automata, which provide linear collision treatment. Building on this framework, we propose a quantum lattice Boltzmann method that relies on linear operations with medium dimensionality. We validated the algorithm through comprehensive simulations of benchmark cases, including vortex-pair merging and decaying turbulence on $2048^2$ computational grid points. The results demonstrate remarkable agreement with direct numerical simulation, effectively capturing the essential nonlinear mechanisms of fluid dynamics. This work offers valuable insights into developing quantum algorithms for other nonlinear problems, and potentially advances the application of quantum computing across various transport phenomena in engineering.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Efficient quantum state tomography with Chebyshev polynomials

    quant-ph 2025-09 conditional novelty 6.0 of 10

    A quantum state tomography method that estimates Chebyshev coefficients of the encoded function, achieving measurement costs independent of qubit count for smooth, large-scale-dominated functions.

  2. Simulating fluid vortex interactions on a superconducting quantum processor

    quant-ph 2025-06 conditional novelty 6.0 of 10

    A data-driven quantum vortex method with spatiotemporal encoding simulates leapfrogging vortex interactions on an 8-qubit superconducting processor, using an evolution operator fitted to classical simulation data.

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