Variational quantum dynamics of two-dimensional rotor models

TL;DR

This paper introduces a neural-network-based numerical method for simulating the non-equilibrium dynamics of large two-dimensional quantum rotor systems, enabling accurate analysis of quantities like return probability and vorticity oscillations.

Contribution

It develops a novel continuous-basis neural network approach combined with Hamiltonian Monte Carlo for simulating large-scale 2D quantum rotor dynamics.

Findings

Simulates systems of up to 64 rotors (8x8)

Accurately captures return probability and vorticity oscillations

Bridges the gap between simulation and experimental system sizes

Abstract

We present a numerical method to simulate the dynamics of continuous-variable quantum many-body systems. Our approach is based on custom neural-network many-body quantum states. We focus on dynamics of two-dimensional quantum rotors and simulate large experimentally relevant system sizes by representing a trial state in a continuous basis and using state-of-the-art sampling approaches based on Hamiltonian Monte Carlo. We demonstrate the method can access quantities like the return probability and vorticity oscillations after a quantum quench in two-dimensional systems of up to 64 (8 $\times$ 8) coupled rotors. Our approach can be used for accurate nonequilibrium simulations of continuous systems at previously unexplored system sizes and evolution times, bridging the gap between simulation and experiment.