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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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ipie: A Python-Based Auxiliary-Field Quantum Monte Carlo Program with Flexibility and Efficiency on CPUs and GPUs.

Fionn D Malone1, Ankit Mahajan2, James S Spencer3

  • 1Google Research, Venice, California 90291, United States.

Journal of Chemical Theory and Computation
|December 12, 2022
PubMed
Summary
This summary is machine-generated.

We developed ipie, a Python-based auxiliary-field quantum Monte Carlo (AFQMC) program, offering competitive performance on CPUs and GPUs. This tool accurately calculates chemical reaction energies, like the [Cu2O2]2+ isomerization, even for complex systems.

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Area of Science:

  • Computational Chemistry
  • Quantum Mechanics
  • Materials Science

Background:

  • Accurate quantum mechanical calculations are crucial for understanding chemical reactions and material properties.
  • Auxiliary-field quantum Monte Carlo (AFQMC) is a powerful method for tackling electron correlation problems.
  • Efficient implementations are needed to apply AFQMC to larger and more complex systems.

Purpose of the Study:

  • To introduce ipie, a new Python-based AFQMC program.
  • To benchmark ipie's performance against existing codes on both CPUs and GPUs.
  • To apply ipie to calculate the isomerization energy of the [Cu2O2]2+ complex.

Main Methods:

  • Development of a Python-based AFQMC program (ipie) with CPU and GPU implementations.
  • Integration of ipie with the PySCF electronic structure package.
  • Benchmarking ipie against C++ codes (QMCPACK, Dice) for various chemical systems.
  • Application of phaseless-AFQMC (ph-AFQMC) with selected configuration interaction trial wave functions to [Cu2O2]2+ isomerization.

Main Results:

  • ipie demonstrates competitive or superior performance compared to C++ AFQMC codes on both CPUs and GPUs.
  • Accurate convergence of the [Cu2O2]2+ isomerization energy to known results was achieved using ph-AFQMC with modest computational resources.
  • The isomerization energy for a large system ([Cu2O2]2+ with a quadruple-zeta basis set) was calculated with high accuracy (error < 1 kcal/mol).

Conclusions:

  • ipie is a versatile and efficient tool for large-scale AFQMC calculations.
  • ph-AFQMC, as implemented in ipie, is effective for systems with significant static and dynamic correlation.
  • The developed program facilitates the study of complex chemical systems and reactions.