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

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.