Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom Arrays

TL;DR

This paper demonstrates that pretraining neural network wavefunction ansätze on limited measurement data accelerates variational Monte Carlo simulations of Rydberg atom arrays, enhancing quantum state reconstruction efficiency.

Contribution

It introduces a method of pretraining RNN-based wavefunction ansätze with small datasets to speed up VMC optimization in Rydberg atom array simulations.

Findings

Pretraining reduces VMC convergence time significantly.

Neural network wavefunctions can be effectively pretrained on limited data.

Measurement data from experiments can enhance neural network-based quantum simulations.

Abstract

Rydberg atom arrays are programmable quantum simulators capable of preparing interacting qubit systems in a variety of quantum states. Due to long experimental preparation times, obtaining projective measurement data can be relatively slow for large arrays, which poses a challenge for state reconstruction methods such as tomography. Today, novel groundstate wavefunction ans\"atze like recurrent neural networks (RNNs) can be efficiently trained not only from projective measurement data, but also through Hamiltonian-guided variational Monte Carlo (VMC). In this paper, we demonstrate how pretraining modern RNNs on even small amounts of data significantly reduces the convergence time for a subsequent variational optimization of the wavefunction. This suggests that essentially any amount of measurements obtained from a state prepared in an experimental quantum simulator could provide significant value for neural-network-based VMC strategies.