Uniform Semiclassical Observable Error Bound of Trotter–Suzuki Splitting: A Simple Algebraic Proof

IF 3.2 2区 数学 Q1 MATHEMATICS, APPLIED SIAM Journal on Numerical Analysis Pub Date : 2026-06-19 DOI:10.1137/25m1777098
Di Fang, Conrad Qu
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Abstract

SIAM Journal on Numerical Analysis, Volume 64, Issue 3, Page 876-899, June 2026.
Abstract. Efficient simulation of the semiclassical Schrödinger equation has garnered significant attention in the numerical analysis community. While controlling the error in the unitary evolution or the wavefunction typically requires the time step size to shrink as the semiclassical parameter [math] decreases, it has been observed–and proved for first- and second-order Trotterization schemes–that the error in certain classes of observables admits a time step size independent of [math]. In this work, we explicitly characterize this class of observables and present a new, simple algebraic proof of uniform-in-[math] error bounds for arbitrarily high-order Trotterization schemes. Our proof relies solely on the algebraic structure of the underlying operators in both the continuous and discrete settings. Unlike previous analyses, it avoids Egorov-type theorems and bypasses heavy semiclassical machinery. To the best of our knowledge, this is the first proof of uniform-in-[math] observable error bounds for Trotterization in the semiclassical regime that relies only on algebraic structure, without invoking the semiclassical limit.
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Trotter-Suzuki分裂的一致半经典可观测误差界:一个简单的代数证明
SIAM数值分析杂志,64卷,第3期,876-899页,2026年6月。摘要。半经典Schrödinger方程的有效模拟在数值分析界引起了极大的关注。虽然控制一元演化或波函数中的误差通常需要随着半经典参数[math]的减小而缩小时间步长,但已经观察到-并证明了一阶和二阶Trotterization方案-某些可观测对象中的误差允许独立于[math]的时间步长。在这项工作中,我们明确地描述了这类可观察对象,并提出了一个新的、简单的代数证明,证明了任意高阶trotter化方案的一致[数学]误差界限。我们的证明仅仅依赖于连续和离散情况下底层算子的代数结构。与以前的分析不同,它避免了egorov型定理,并绕过了繁重的半经典机制。据我们所知,这是第一次证明在只依赖于代数结构而不调用半经典极限的半经典状态下Trotterization的均匀性-[数学]可观察到的误差界限。
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来源期刊
CiteScore
4.80
自引率
6.90%
发文量
110
审稿时长
4-8 weeks
期刊介绍: SIAM Journal on Numerical Analysis (SINUM) contains research articles on the development and analysis of numerical methods. Topics include the rigorous study of convergence of algorithms, their accuracy, their stability, and their computational complexity. Also included are results in mathematical analysis that contribute to algorithm analysis, and computational results that demonstrate algorithm behavior and applicability.
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