Quantum Trajectories with Dynamic Loop Scheduling and Reinforcement Learning

Author

Carino, R.L. ; Banicescu, Joana ; Pabico, Jaderick P. ; Rashid, Mahbubur

Author_Institution

Center for Comput. Sci., Mississippi State Univ.

fYear

2005

fDate

Sept. 2005

Firstpage

1

Lastpage

2

Abstract

The study of many problems in quantum mechanics is based on finding the solution to the time-dependent Schrodinger equation which describes the dynamics of quantum-mechanical systems composed of a particle of mass m moving in a potential V. Based on the hydrodynamic interpretation of quantum mechanics by Bohm (1952), an unstructured grid approach, the quantum trajectory method (QTM) was developed by Lopreore and Wyatt (1999). Derivatives needed for updating the equations of motion are obtained using curve-fitting by a moving weighted least squares algorithm, and analytically differentiating the least squares curves. The calculations involve computationally-intensive parallel loops with nonuniform iterate execution times

Keywords

Schrodinger equation; learning (artificial intelligence); parallel programming; physics computing; program control structures; quantum theory; Schrodinger equation; curve-fitting; dynamic loop scheduling; moving weighted least squares algorithm; nonuniform iterate execution times; parallel loops; quantum mechanics; quantum trajectory; reinforcement learning; unstructured grid approach; Algorithm design and analysis; Curve fitting; Differential equations; Dynamic scheduling; Hydrodynamics; Learning; Least squares methods; Motion analysis; Quantum mechanics; Schrodinger equation;

fLanguage

English

Publisher

ieee

Conference_Titel

Cluster Computing, 2005. IEEE International

Conference_Location

Burlington, MA

ISSN

1552-5244

Print_ISBN

0-7803-9486-0

Electronic_ISBN

1552-5244

Type

conf

DOI

10.1109/CLUSTR.2005.347015

Filename

4154143