Gradient optimization of fermionic projected entangled pair states on directed lattices

Abstract

The recently developed stochastic gradient method combined with Monte Carlo sampling techniques [PRB {\bf 95}, 195154 (2017)] offers a low scaling and accurate method to optimize the projected entangled pair states (PEPS). We extended this method to the fermionic PEPS (fPEPS). To simplify the implementation, we introduce a fermi arrow notation to specify the order of the fermion operators in the virtual entangled EPR pairs. By defining some local operation rules associated with the fermi arrows, one can implement fPEPS algorithms very similar to that of standard PEPS. We benchmark the method for the interacting spinless fermion models, and the t-J models. The numerical calculations show that the gradient optimization greatly improves the results of simple update method. Furthermore, much larger virtual bond dimensions ($D$) and truncation dimensions ($D_c$) than those of boson and spin systems are necessary to converge the results. The method therefore offer a powerful tool to simulate fermion systems because it has much lower scaling than the direct contraction methods.

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• $2^{1296}$ Exponentially Complex Quantum Many-Body Simulation via Scalable Deep Learning Method
• Fermionic tensor network contraction for arbitrary geometries
• Numerical continuum tensor networks in two dimensions
• Simulation of the Ring-exchange Models with Projected Entangled Pair States
• Solving Fermi-Hubbard-type Models by Tensor Representations of Backflow Corrections
• Neuralized Fermionic Tensor Networks for Quantum Many-Body Systems