Frozen Gaussian Sampling: A Mesh-free Monte Carlo Method For Approximating Semiclassical Schr\"odinger Equations

TL;DR

The paper introduces the Frozen Gaussian Sampling (FGS), a mesh-free Monte Carlo method that efficiently approximates solutions to semiclassical Schrödinger equations, especially in high dimensions and for small semiclassical parameters.

Contribution

It develops a novel mesh-free Monte Carlo algorithm with proven sample complexity independence from the semiclassical parameter, suitable for high-dimensional problems.

Findings

FGS outperforms traditional mesh-based methods in high dimensions.

Sample complexity of FGS is independent of the semiclassical parameter .

Numerical experiments validate FGS's effectiveness in various quantum scenarios.

Abstract

In this paper, we develop a Monte Carlo algorithm named the Frozen Gaussian Sampling (FGS) to solve the semiclassical Schr\"odinger equation based on the frozen Gaussian approximation. Due to the highly oscillatory structure of the wave function, traditional mesh-based algorithms suffer from "the curse of dimensionality", which gives rise to more severe computational burden when the semiclassical parameter \(\ep\) is small. The Frozen Gaussian sampling outperforms the existing algorithms in that it is mesh-free in computing the physical observables and is suitable for high dimensional problems. In this work, we provide detailed procedures to implement the FGS for both Gaussian and WKB initial data cases, where the sampling strategies on the phase space balance the need of variance reduction and sampling convenience. Moreover, we rigorously prove that, to reach a certain accuracy, the number of samples needed for the FGS is independent of the scaling parameter \(\ep\). Furthermore, the complexity of the FGS algorithm is of a sublinear scaling with respect to the microscopic degrees of freedom and, in particular, is insensitive to the dimension number. The performance of the FGS is validated through several typical numerical experiments, including simulating scattering by the barrier potential, formation of the caustics and computing the high-dimensional physical observables without mesh.