A normalising-flow-assisted neural quantum state method estimates ground state energies of Ising chains with up to 50 spins, matching matrix product states for long-range interactions.
Phase diagram of the $J$-$J_d$ Heisenberg Model on the Maple-Leaf Lattice: Neural networks and density matrix renormalization group
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abstract
We microscopically analyze the nearest neighbor Heisenberg model on the maple-leaf lattice through neural quantum states (NQS) and infinite density matrix renormalization group (iDMRG). Embarking to parameter regimes beyond the exact dimer singlet ground state with a dimer bond spin exchange coupling $J_d$ varied against the exchange strength $J$ of all other bonds, iDMRG (NQS) finds a dimer state paramagnetic phase for $J_d/J > 1.464$ ($J_d/J > 1.39$) and a canted $120^\circ$ magnetic order for $J_d/J < 1.419$ ($J_d/J < 1.23$). Assessing training convergence inaccuracies of NQS and the influence of finite cylindric circumference for iDMRG, we discuss the possible existence of an intermediate phase between magnet and dimer paramagnet.
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Improved Ground State Estimation in Quantum Field Theories via Normalising Flow-Assisted Neural Quantum States
A normalising-flow-assisted neural quantum state method estimates ground state energies of Ising chains with up to 50 spins, matching matrix product states for long-range interactions.