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Simulating the Two-Dimensional t-J Model at Finite Doping with Neural Quantum States.

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This summary is machine-generated.

We developed Gutzwiller projected hidden fermion determinant states (G-HFDS) for simulating strongly interacting fermion systems. This efficient method provides new insights into the Fermi-Hubbard model across all doping levels.

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Area of Science:

  • Condensed Matter Physics
  • Quantum Many-Body Systems
  • Computational Physics

Background:

  • Simulating large, strongly interacting fermionic systems is computationally demanding.
  • Existing numerical methods struggle with the complexity of these systems.

Purpose of the Study:

  • Introduce a novel, efficient method for simulating strongly interacting fermionic systems.
  • Investigate the low-energy physics of the t-J model across the entire doping regime.

Main Methods:

  • Developed Gutzwiller projected hidden fermion determinant states (G-HFDS).
  • Applied G-HFDS to the strongly interacting limit of the Fermi-Hubbard model (t-J model).
  • Analyzed spin and polaron correlation functions and Fermi surfaces.

Main Results:

  • G-HFDS achieve competitive energies with matrix product states on large lattices (10x10).
  • G-HFDS use significantly fewer parameters, enabling simulations of larger systems.
  • Tracked the evolution of magnetic polarons and emergent quasiparticles with doping.

Conclusions:

  • G-HFDS offer an efficient approach for simulating large-scale fermionic systems.
  • The method provides new insights into the interplay of kinetic and magnetic interactions.
  • Determinant-based neural quantum states with fermionic sign structure show great potential.