Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation

Randy A. Bartels; Margaret M. Murnane; Henry C. Kapteyn; Ivan Christov; Herschel Rabitz

doi:10.1103/PhysRevA.70.043404

Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation

Randy A. Bartels, Margaret M. Murnane, Henry C. Kapteyn, Ivan Christov, Herschel Rabitz

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53 Scopus citations

Abstract

The use of ensemble behavior of population involved in a learning control optimization process for extracting the temporal dynamics of a high-field quantum process was discussed. The optimization of high-harmonic generation (HHG) was used to generate and analyze a large data set. It was found that the quantum interferences occured during the HHG process were optimized by adjusting the phase of laser field guided by a learning algorithm. The data collected illustrated the attosecond dynamics of HHG and uncover an anomalous region of parameter space.

Original language	English (US)
Article number	043404
Pages (from-to)	043404-1-043404-5
Journal	Physical Review A - Atomic, Molecular, and Optical Physics
Volume	70
Issue number	4
DOIs	https://doi.org/10.1103/PhysRevA.70.043404
State	Published - Oct 2004

All Science Journal Classification (ASJC) codes

Atomic and Molecular Physics, and Optics

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10.1103/PhysRevA.70.043404

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title = "Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation",

abstract = "The use of ensemble behavior of population involved in a learning control optimization process for extracting the temporal dynamics of a high-field quantum process was discussed. The optimization of high-harmonic generation (HHG) was used to generate and analyze a large data set. It was found that the quantum interferences occured during the HHG process were optimized by adjusting the phase of laser field guided by a learning algorithm. The data collected illustrated the attosecond dynamics of HHG and uncover an anomalous region of parameter space.",

author = "Bartels, {Randy A.} and Murnane, {Margaret M.} and Kapteyn, {Henry C.} and Ivan Christov and Herschel Rabitz",

note = "Funding Information: The authors gratefully acknowledge support for this work from the National Science Foundation and from the Chemical Sciences, Geosciences and Biosciences Division of the Office of Basic Energy Sciences, U.S. Department of Energy.",

year = "2004",

month = oct,

doi = "10.1103/PhysRevA.70.043404",

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T1 - Learning from learning algorithms

T2 - Application to attosecond dynamics of high-harmonic generation

AU - Bartels, Randy A.

AU - Murnane, Margaret M.

AU - Kapteyn, Henry C.

AU - Christov, Ivan

AU - Rabitz, Herschel

N1 - Funding Information: The authors gratefully acknowledge support for this work from the National Science Foundation and from the Chemical Sciences, Geosciences and Biosciences Division of the Office of Basic Energy Sciences, U.S. Department of Energy.

PY - 2004/10

Y1 - 2004/10

N2 - The use of ensemble behavior of population involved in a learning control optimization process for extracting the temporal dynamics of a high-field quantum process was discussed. The optimization of high-harmonic generation (HHG) was used to generate and analyze a large data set. It was found that the quantum interferences occured during the HHG process were optimized by adjusting the phase of laser field guided by a learning algorithm. The data collected illustrated the attosecond dynamics of HHG and uncover an anomalous region of parameter space.

AB - The use of ensemble behavior of population involved in a learning control optimization process for extracting the temporal dynamics of a high-field quantum process was discussed. The optimization of high-harmonic generation (HHG) was used to generate and analyze a large data set. It was found that the quantum interferences occured during the HHG process were optimized by adjusting the phase of laser field guided by a learning algorithm. The data collected illustrated the attosecond dynamics of HHG and uncover an anomalous region of parameter space.

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Learning from learning algorithms: Application to attosecond dynamics of high-harmonic generation

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