numerical analysis

A low-rank hierarchical framework for the non-Markovian stochastic Schrödinger equation with convergence analysis

arXiv:2607.14689

summary

The paper introduces a low‑rank hierarchical numerical method for solving the non‑Markovian stochastic Schrödinger equation, provides a rigorous convergence analysis, and shows that it generalizes the Hierarchy of Pure States while remaining computationally efficient.

Abstract

We propose and analyze a novel numerical framework for the non-Markovian stochastic Schrödinger equation (NMSSE) based on a low-rank approximation of the bath correlation functions. By decomposing the memory kernel into a finite-dimensional representation, we derive a truncated system of hierarchical equations that effectively balances computational tractability with physical fidelity. A rigorous convergence analysis is established for the hierarchical framework under mild assumptions. We demonstrate that our formulation serves as a mathematical generalization of the Hierarchy of Pure States (HOPS), encompassing it as a special case while offering a more flexible representation of non-Markovian effects. Numerical experiments across several benchmark models are presented to illustrate the validity and efficacy of the proposed method.

Topics & keywords

#low-rank approximation#non-markovian dynamics#stochastic schrödinger equation#hierarchical equations#convergence analysisbath correlation functionshierarchy of pure statesNMSSEtruncated hierarchical systemnumerical experiments
A low-rank hierarchical framework for the non-Markovian stochastic Schrödinger equation with convergence analysis · wovepaper