Data-Enhanced Variational Monte Carlo Simulations for Rydberg Atom Arrays

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 ansätze 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.

7 pages, 3 figures

References in corpus (5)

• Efficient quantum state tomography
• Competing density-wave orders in a one-dimensional hard-boson model
• Quantum phases of Rydberg atoms on a kagome lattice
• Stochastic series expansion method for quantum Ising models with arbitrary interactions
• Fast Preparation and Detection of a Rydberg Qubit using Atomic Ensembles