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Cooling an Optically Trapped Ultracold Fermi Gas by Periodical Driving
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Tensor-Train Split-Operator Fourier Transform (TT-SOFT) Method: Multidimensional Nonadiabatic Quantum Dynamics.

Samuel M Greene1,2, Victor S Batista1,2

  • 1Department of Chemistry, Yale University , P.O. Box 208107, New Haven, Connecticut 06520-8107, United States.

Journal of Chemical Theory and Computation
|August 2, 2017
PubMed
Summary
This summary is machine-generated.

We developed the tensor-train split-operator Fourier transform (TT-SOFT) method for efficient quantum dynamics simulations. This approach accurately models complex molecular systems by avoiding exponential scaling issues in quantum calculations.

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Area of Science:

  • Quantum Chemistry
  • Computational Physics
  • Theoretical Chemistry

Background:

  • Simulating multidimensional nonadiabatic quantum dynamics is computationally challenging.
  • Full-rank grid-based methods suffer from exponential scaling with dimensionality.
  • Efficient methods are needed for understanding complex molecular processes.

Purpose of the Study:

  • Introduce the tensor-train split-operator Fourier transform (TT-SOFT) method.
  • Implement the grid-based SOFT method using dynamically adaptive tensor-train representations.
  • Enable efficient simulations of multidimensional quantum dynamics.

Main Methods:

  • Developed TT-SOFT by combining SOFT with tensor-train representations.
  • Utilized matrix product states principles for wave function representation.
  • Propagated wave packets and computed observables without full-rank grid computation.

Main Results:

  • Demonstrated accuracy and efficiency of TT-SOFT for 24-dimensional wave packet propagation.
  • Applied TT-SOFT to simulate pyrazine S1/S2 interconversion dynamics after UV excitation.
  • Showcased TT-SOFT's ability to overcome exponential scaling limitations.

Conclusions:

  • TT-SOFT is a powerful computational tool for quantum dynamics simulations.
  • The method is suitable for complex polyatomic systems.
  • TT-SOFT avoids the exponential scaling problem inherent in full-rank grid-based methods.