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Title: Systems and methods for determining optimal parameters for dynamic quantum clustering analyses

Abstract

In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrodinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.

Inventors:
;
Publication Date:
Research Org.:
SLAC National Accelerator Lab., Menlo Park, CA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1493569
Patent Number(s):
10,169,445
Application Number:
14/492,677
Assignee:
The Board of Trustees of the Leland Stanford Junior University (Stanford, CA) (United States) SLAC
DOE Contract Number:  
AC02-76SF00515
Resource Type:
Patent
Resource Relation:
Patent File Date: 2014 Sep 22
Country of Publication:
United States
Language:
English
Subject:
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSICS

Citation Formats

Weinstein, Marvin, and Horn, David. Systems and methods for determining optimal parameters for dynamic quantum clustering analyses. United States: N. p., 2019. Web.
Weinstein, Marvin, & Horn, David. Systems and methods for determining optimal parameters for dynamic quantum clustering analyses. United States.
Weinstein, Marvin, and Horn, David. Tue . "Systems and methods for determining optimal parameters for dynamic quantum clustering analyses". United States. https://www.osti.gov/servlets/purl/1493569.
@article{osti_1493569,
title = {Systems and methods for determining optimal parameters for dynamic quantum clustering analyses},
author = {Weinstein, Marvin and Horn, David},
abstractNote = {In the present work, quantum clustering is extended to provide a dynamical approach for data clustering using a time-dependent Schrodinger equation. To expedite computations, we can approximate the time-dependent Hamiltonian formalism by a truncated calculation within a set of Gaussian wave-functions (coherent states) centered around the original points. This allows for analytic evaluation of the time evolution of all such states, opening up the possibility of exploration of relationships among data points through observation of varying dynamical-distances among points and convergence of points into clusters. This formalism may be further supplemented by preprocessing, such as dimensional reduction through singular value decomposition and/or feature filtering. Additionally, the parameters of the analysis can be modified in order to improve the efficiency of the dynamic quantum clustering processes.},
doi = {},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {1}
}

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