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Compression algorithm for multideterminant wave functions.

Gihan L Weerasinghe1, Pablo López Ríos1, Richard J Needs1

  • 1Theory of Condensed Matter Group, Cavendish Laboratory, J J Thomson Avenue, Cambridge CB3 0HE, United Kingdom.

Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics
|October 30, 2014
PubMed
Summary
This summary is machine-generated.

A new compression algorithm significantly reduces the computational cost of quantum Monte Carlo calculations by minimizing the number of determinants evaluated. This method shows sublinear scaling, improving efficiency for complex wave function evaluations.

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Area of Science:

  • Quantum chemistry
  • Computational physics

Background:

  • Quantum Monte Carlo (QMC) calculations often involve large numbers of determinants in multideterminant wave functions.
  • Evaluating these determinants is computationally expensive, limiting the scale and scope of QMC simulations.

Purpose of the Study:

  • To introduce a novel compression algorithm for multideterminant wave functions.
  • To reduce the computational cost associated with evaluating these wave functions in QMC calculations.
  • To investigate the scaling properties of QMC methods when employing this compression.

Main Methods:

  • Development of a three-level compression algorithm for multideterminant wave functions.
  • Application of the algorithm to reduce the number of determinants in QMC calculations.
  • Analysis of computational cost reduction and scaling behavior.

Main Results:

  • The compression algorithm effectively reduces the number of determinants to be evaluated.
  • Computational costs were reduced by factors ranging from 2 to over 25 in studied examples.
  • Evidence of sublinear scaling of QMC calculations with the number of determinants was observed when using the compression algorithm.

Conclusions:

  • The developed compression algorithm offers a significant improvement in the efficiency of QMC calculations.
  • The algorithm provides excellent results even at its least computationally intensive level, operating in polynomial time.
  • The findings suggest a more scalable approach to complex quantum mechanical simulations using QMC.