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Quantum-Assisted Variational Monte Carlo.

Longfei Chang1, Zhendong Li1, Wei-Hai Fang1

  • 1Key Laboratory of Theoretical and Computational Photochemistry, Ministry of Education, College of Chemistry, Beijing Normal University, Beijing 100875, China.

Precision Chemistry
|September 26, 2025
PubMed
Summary
This summary is machine-generated.

A new quantum-assisted variational Monte Carlo (QA-VMC) algorithm enhances classical methods for solving quantum many-body systems. This quantum approach improves sampling efficiency and speeds up convergence to the ground state.

Keywords:
neural-network quantum statesquantum algorithmsquantum-enhanced Markov chain Monte Carlostrongly correlated systemsvariational Monte Carlo

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Area of Science:

  • Quantum Many-Body Physics
  • Computational Chemistry
  • Quantum Computing Algorithms

Background:

  • Solving the ground state of quantum many-body systems is a significant challenge in physics and chemistry.
  • Quantum hardware advancements offer new approaches to tackle these complex systems.
  • Classical methods like variational Monte Carlo (VMC) are computationally intensive.

Purpose of the Study:

  • To introduce a quantum-assisted variational Monte Carlo (QA-VMC) algorithm for solving quantum many-body ground states.
  • To investigate if quantum-assisted proposals offer a computational advantage over classical methods.
  • To assess the efficiency and accuracy of QA-VMC for complex systems.

Main Methods:

  • Adapted the quantum-enhanced Markov chain Monte Carlo (QeMCMC) algorithm for VMC.
  • Developed a QA-VMC algorithm to sample neural-network wave function distributions.
  • Performed numerical investigations on the Fermi-Hubbard model and molecular systems.

Main Results:

  • The QA-VMC algorithm demonstrated larger spectral gaps and reduced autocorrelation times compared to classical proposals.
  • Achieved more efficient sampling and faster convergence to the ground state.
  • Obtained more accurate and precise estimations of physical observables, especially for specific parameter ranges.

Conclusions:

  • Quantum-assisted algorithms show potential for enhancing classical variational methods.
  • QA-VMC offers a promising avenue for more efficient and accurate solutions to quantum many-body problems.
  • The developed algorithm could accelerate scientific discovery in physics and chemistry.