Adaptive Lattice Gas Algorithm: Classical and Quantum implementations
arXiv:2504.13549 · doi:10.1103/l51d-dpb6
Abstract
Lattice gas algorithms (LGA) are a class of algorithms including, in chronological order, binary lattice gas cellular automata (LGCA), integer lattice gas algorithms (ILGA) and lattice Boltzmann method (LBM). They are largely used for simulating non-linear systems. Starting from 1-dimensional ILGA, we design an algorithm where we carry out a fraction of the possible collisions. These fractions are then adapted to reproduce LBM equilibrium distributions, resulting in an adaptive lattice gas algorithm (ALGA) that achieves the same simulation results of LBM. Considering this, we develop a quantum algorithm that involves a linear collision operator and capable of simulating the same phenomena, while still using a measurement and reinitialization procedure. Multi-time-step implementation is possible in some specific cases briefly discussed.
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