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Compression algorithm for discrete light-cone quantization.

Xiao Pu1, Sophia S Chabysheva1, John R Hiller1

  • 1Department of Physics, University of Minnesota-Duluth, Duluth, Minnesota 55812, USA.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|February 4, 2014
PubMed
Summary
This summary is machine-generated.

We adapted a compression algorithm for spin lattice Hamiltonians to light-front field theories. This method efficiently represents and compresses Hamiltonian eigenvectors using singular value decomposition for quantum field theory calculations.

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Area of Science:

  • Quantum Field Theory
  • Computational Physics

Background:

  • Spin lattice Hamiltonians are complex systems.
  • Efficiently representing Hamiltonian eigenvectors is crucial for solving quantum field theory problems.

Purpose of the Study:

  • To adapt a known compression algorithm for spin lattice Hamiltonians.
  • To apply this algorithm to light-front field-theoretic Hamiltonians.

Main Methods:

  • Adapted the Weinstein, Auerbach, and Chandra compression algorithm.
  • Used discrete light-cone quantization (DLCQ) for matrix representation.
  • Represented eigenvectors using singular value decomposition (SVD) of 2D arrays.
  • Compressed eigenvectors via SVD truncation.
  • Decomposed the rank-four Hamiltonian tensor into factorized matrices.

Main Results:

  • Successfully adapted the compression algorithm for light-front Hamiltonians.
  • Demonstrated the method's utility in a model theory.
  • Efficient representation and compression of Hamiltonian eigenvectors achieved.

Conclusions:

  • The adapted algorithm provides an efficient method for handling Hamiltonian eigenvectors in light-front field theory.
  • Singular value decomposition offers a powerful tool for compressing quantum field theory data.
  • This approach facilitates computational solutions for complex field theory problems.