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- W4229366182 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 ground-state wave-function Ansatze 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 wave function. This suggests that essentially any amount of measurements obtained from a state prepared in an experimental quantum simulator could provide significant values for neural-network-based VMC strategies." @default.
- W4229366182 created "2022-05-10" @default.
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- W4229366182 date "2022-05-09" @default.
- W4229366182 modified "2023-10-18" @default.
- W4229366182 title "Data-enhanced variational Monte Carlo simulations for Rydberg atom arrays" @default.
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- W4229366182 doi "https://doi.org/10.1103/physrevb.105.205108" @default.
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