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.

Neural Annealing and Visualization of Autoregressive Neural Networks in the Newman–Moore Model

“…Inspired by the successes of ANNs in computer science, physicists have also started to use them to study problems in various branches of physics [2], such as optics, cosmology, quantum information, and condensed matter. In the latter, they are used to identify phases of matter [3][4][5][6][7], increase the performance of Monte Carlo simulations [8][9][10][11][12][13][14], and find precise representations of the ground state of quantum systems [15][16][17][18]. A particular aspect of their capacity to characterize quantum matter [19] is their ability to be used as parameterized functions to represent the underlying probability distribution of a physical system.…”

Neural annealing and visualization of autoregressive neural networks in the Newman-Moore model

“…Inspired by the successes of ANNs in computer science, physicists have also started to use them to study problems in various branches of physics [2] such as optics, cosmology, quantum information and condensed matter. In the latter, they are used to identify phases of matter [3][4][5][6][7] and, increase the performance of Monte Carlo simulations [8][9][10][11][12][13][14] and, find precise representations of the ground state of quantum systems [15][16][17][18]. A particular aspect of their capacity to characterize quantum matter [19] is their ability to be used as parameterized functions to represent the underlying probability distribution of a physical system.…”

Unsupervised Learning of Rydberg Atom Array Phase Diagram with Siamese Neural Networks

“…Experimental states have been reconstructed using neural network based tomography [71]. Ground states have been calculated [72] and simulated measurement data has been used for pre-training variational wave functions [73].…”