Conserved and relaxing modes enable polynomial-success coherent Carleman lattice Boltzmann evolution
Preprint, 2026

Carleman linearization represents nonlinear lattice Boltzmann collision dynamics in a form amenable to quantum block encoding, but low postselection probabilities in existing population-space encodings make coherent multi-step evolution exponentially unlikely to succeed. We present a quantum Carleman lattice Boltzmann scheme based on the multi-relaxation-time collision operator in a weighted Hermite moment basis. In this basis, the lifted collision operator separates into small coupled blocks whose norm exceeds unity because of the coupling between conserved and relaxing modes. This block structure enables a norm-attaining block encoding with a single-step success probability close to unity. Crucially, scaling the pair sector for a prescribed evolution horizon makes the combined encoding and sector-selection overhead grow polynomially rather than exponentially with the number of steps. For a hundred-step evolution, the resulting complete-run success probability exceeds that of population-space encodings by orders of magnitude. Over horizons of several tens of steps, the truncated dynamics reproduces the nonlinearly generated modes of a benchmark flow to within a few percent. We verify the combined preparation, evolution and readout protocol as a single unitary circuit and give a complete gate-level resource estimate for extracting a nonlinear observable of the benchmark flow from a coherent Carleman evolution. The block structure of conserved and relaxing modes holds equally on three-dimensional lattices and provides a constructive route to norm-attaining encodings for lattice Boltzmann models with quadratic equilibria.

Author

Huadong Yao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Sauro Succi

Istituto Italiano di Tecnologia

GEneric Multidiscaplinary optimization for sail INstallation on wInd-assisted ships (GEMINI)

Swedish Transport Administration (2023/32107), 2023-09-01 -- 2026-08-31.

Subject Categories (SSIF 2025)

Fluid Mechanics

Computer Engineering

Computational Mathematics

Statistical physics and complex systems

DOI

10.48550/arXiv.2609.32310

More information

Created

9/29/2026