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Area of Science:

  • Quantum mechanics
  • Condensed matter physics
  • Quantum chemistry

Background:

  • Simulating real-time evolution of many-electron quantum systems is crucial but computationally challenging.
  • Existing mean-field approximations often fail to capture essential many-body correlations.

Purpose of the Study:

  • Introduce a novel variational approach for fermionic time-dependent wave functions.
  • Improve accuracy in simulating quantum system dynamics by including many-body correlations.

Main Methods:

  • Utilized time-dependent Jastrow factors and backflow transformations.
  • Employed neural networks for parameterization.
  • Applied time-dependent variational Monte Carlo and a new Taylor-root expansion method for parameter optimization.

Main Results:

  • Demonstrated the approach on three distinct quantum systems.
  • Observed clear signatures of many-body correlations in the simulated dynamics.
  • Showcased accurate real-time evolution descriptions.

Conclusions:

  • The developed variational approach effectively captures many-body correlations.
  • Provides insights into quantum dynamics beyond mean-field limitations.
  • Offers a more accurate computational tool for studying interacting electronic systems.