Quantum Brain
← Back to papers

Time complexity analysis of quantum difference methods for the multiscale transport equations

He Xiaoyang, Jin Shi, Yubei Yue·November 12, 2022·DOI: 10.48550/arXiv.2211.06593
PhysicsComputer ScienceMathematics

AI Breakdown

Get a structured breakdown of this paper — what it's about, the core idea, and key takeaways for the field.

Abstract

We investigate time complexities of finite difference methods for solving the multiscale transport equation with quantum algorithms. We find that the time complexities of both the classical treatment and quantum treatment for a standard explicit scheme scale as $\mathcal{O}(1/\varepsilon)$, where $\varepsilon$ is the small scaling parameter, while the complexities for the even-odd parity based Asymptotic-Preserving (AP) scheme do not depend on $\varepsilon$. This indicates that it is still of great importance to use AP (and probably other efficient multiscale) schemes for multiscale problems in quantum computing when solving multiscale transport or kinetic equations.

Related Research

Quantum Intelligence

Ask about quantum research, companies, or market developments.