David Jennings, Kamil Korzekwa, Matteo Lostaglio, Richard Ashworth, Emanuele Marsili, Stephen Rolston (Dec 04 2025).
Abstract: Computational fluid dynamics (CFD) is a cornerstone of classical scientific computing, and there is growing interest in whether quantum computers can accelerate such simulations. To date, the existing proposals for fault-tolerant quantum algorithms for CFD have almost exclusively been based on the Carleman embedding method, used to encode nonlinearities on a quantum computer. In this work, we begin by showing that these proposals suffer from a range of severe bottlenecks that negate conjectured quantum advantages: lack of convergence of the Carleman method, prohibitive time-stepping requirements, unfavorable condition number scaling, and inefficient data extraction. With these roadblocks clearly identified, we develop a novel algorithm for the incompressible lattice Boltzmann equation that circumvents these obstacles, and then provide a detailed analysis of our algorithm, including all potential sources of algorithmic complexity, as well as gate count estimates. We find that for an end-to-end problem, a modest quantum advantage may be preserved for selected observables in the high-error-tolerance regime. We lower bound the Reynolds number scaling of our quantum algorithm in dimension
D at Kolmogorov microscale resolution with
O(Re43(1+2D)×qM), where
qM is a multiplicative overhead for data extraction with
qM=O(Re83) for the drag force. This upper bounds the scaling improvement over classical algorithms by
O(Re83D). However, our numerical investigations suggest a lower speedup, with a scaling estimate of
O(Re1.936×qM) for
D=2. Our results give robust evidence that small, but nontrivial, quantum advantages can be achieved in the context of CFD, and motivate the need for additional rigorous end-to-end quantum algorithm development.
