Quantum Element Method for Simulation of Quantum Eigenvalue Problems

TL;DR

The paper introduces the quantum element method (QEM), which partitions quantum eigenvalue problems into subdomains, uses reduced order models with POD basis functions, and applies domain decomposition for fast, accurate simulations of quantum structures.

Contribution

The study develops the quantum element method (QEM) combining domain decomposition and POD-based reduced models for efficient quantum eigenvalue problem simulation.

Findings

QEM significantly reduces degrees of freedom needed for accurate solutions.

QEM achieves high accuracy beyond training conditions.

Demonstrated effectiveness on quantum-well structures.

Abstract

A previously developed quantum reduced-order model is revised and applied, together with the domain decomposition, to develop the quantum element method (QEM), a methodology for fast and accurate simulation of quantum eigenvalue problems. The concept of the QEM is to partition the simulation domain of a quantum eigenvalue problem into smaller subdomains that are referred to as elements. These elements could be the building blocks for quantum structures of interest. Each of the elements is projected onto a functional space represented by a reduced order model, which leads to a quantum Hamiltonian equation in the functional space for each element. The basis functions in this study is generated from proper orthogonal decomposition (POD). To construct a POD model for a large domain, these projected elements are combined together, and the interior penalty discontinuous Galerkin method is applied to stabilize the numerical solution and to achieve the interface continuity. The POD is able to optimize the basis functions (or POD modes) specifically tailored to the geometry and parametric variations of the problem and can therefore substantially reduce the degree of freedom (DoF) needed to solve the Schr\"odinger equation. The proposed multi-element POD model (or QEM) is demonstrated in several quantum-well structures with a focus on understanding how to achieve accurate prediction of WFs with a small numerical DoF. It has been shown that the QEM is able to achieve a substantial reduction in the DoF with a high accuracy beyond the conditions accounted for in the training of the POD modes.