Two-dimensional Hyperbolic RNN Neural Quantum State

Abstract

In the first part of this work, we construct the first type of two-dimensional (2D) hyperbolic neural quantum state (NQS) in the form of the Lorentz 2DRNN (Recurrent Neural Network) and benchmark its performance against the Euclidean 2DRNN in the paradigmatic $N \times N$ 2D Transverse Field Ising Model (2DTFIM) setting with different lattice sizes up to N = 12 at different transverse magnetic field strengths. We find that hyperbolic Lorentz 2DRNN NQS robustly outperforms Euclidean 2DRNN NQS in our experiments when the system is at the phase transition point, where the physics can be described by a conformal field theory (CFT) - known via the AdS/CFT holographic duality to possess an underlying hyperbolic geometry. In the second part of this work, we benchmark the performances of recently introduced one-dimensional (1D) hyperbolic NQS ansatzes (including Poincare RNN/GRU and Lorentz RNN/GRU) against their Euclidean versions in $N \times N$ 2DTFIM, converted to a one-dimensional sequence. The findings extend our previous results showing that 1D hyperbolic NQS robustly outperforms 1D Euclidean NQS, thanks to the combined effects of the induced 1st and Nth neighbor interaction hierarchy and the CFT physics at criticality. While studies on larger system sizes remain future work, this study serves as a proof-of-concept for the utility, effectiveness, and superior performance of 1D and 2D hyperbolic NQS ansatzes compared to conventional Euclidean NQS models in many-body quantum systems - particularly those exhibiting structural hierarchy, criticality, or a combination of both.