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Updated: Apr 19, 2026

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Multiscale Sampling of a Heterogeneous Water/Metal Catalyst Interface using Density Functional Theory and Force-Field Molecular Dynamics

Published on: April 12, 2019

Quantum lattice Boltzmann method for several time steps: A local Carleman linearization algorithm.

Antonio David Bastida Zamora¹, Ljubomir Budinski¹, Valtteri Lahtinen¹

¹Quanscient Oy, Peltokatu 34 B, 33100 Tampere, Finland.

Physical Review. E

|April 18, 2026

Summary

This summary is machine-generated.

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This study introduces a new quantum Lattice Boltzmann method encoding using Carleman linearization. The novel approach enables local collision rules with improved accuracy for quantum simulations.

Area of Science:

Quantum Computing
Computational Physics
Fluid Dynamics

Background:

The Lattice Boltzmann method is a powerful tool for simulating fluid dynamics.
Quantum algorithms offer potential speedups for complex simulations.
Previous quantum Lattice Boltzmann method encodings faced limitations in collision rule implementation and accuracy.

Purpose of the Study:

To present a novel encoding for the quantum Lattice Boltzmann method algorithm.
To enable local collision rules within the quantum Lattice Boltzmann method.
To improve the accuracy and efficiency of quantum fluid dynamics simulations.

Main Methods:

Utilized Carleman linearization for encoding the quantum Lattice Boltzmann method.
Developed an algorithm allowing for local collision rules.

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Analyzed the computational scaling of the proposed encoding.

Main Results:

Achieved a higher probability of obtaining correct results, on the order of 10^{-2}.
The algorithm exhibits a computational scaling of O[log_{2}^{2}(N)+Q^{3}] per time step.
The encoding is compatible with dynamical circuits using a constant number of qubits.

Conclusions:

The proposed Carleman linearization encoding enhances the quantum Lattice Boltzmann method.
This advancement facilitates more accurate and potentially faster quantum simulations of fluid dynamics.
The method offers a promising direction for future research in quantum computational physics.