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.
Simulating magnetic antiskyrmions on the lattice
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abstract
Magnetic skyrmions are topologically protected spin structures that naturally emerge in magnetic materials. While a vast amount of effort has gone into the study of their properties, their counterpart of opposite topological charge, the antiskyrmion, has not received as much attention. We aim to close this gap by deploying Monte Carlo simulations of spin-lattice systems in order to investigate which interactions support antiskyrmions, as well as skyrmions of Bloch and N\'eel type. We find that a suitable combination of ferromagnetic exchange and Dzyaloshinskii-Moriya (DM) interactions is able to stabilize all three types. Considering a three-dimensional spin lattice model, we provide a finite-temperature phase diagram featuring a stable antiskyrmion lattice phase for a large range of temperatures. In addition, we also shed light on the creation and annihilation processes of these antiskyrmion tubes and study the effects of the DM interaction strength on their typical size.
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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.