Flexible learning of quantum states with generative query neural networks

Yan Zhu, Ya-Dong Wu (), Ge Bai, Dong-Sheng Wang, Yuexuan Wang and Giulio Chiribella ()
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Yan Zhu: The University of Hong Kong
Ya-Dong Wu: The University of Hong Kong
Ge Bai: The University of Hong Kong
Dong-Sheng Wang: Chinese Academy of Sciences
Yuexuan Wang: The University of Hong Kong
Giulio Chiribella: The University of Hong Kong

Nature Communications, 2022, vol. 13, issue 1, 1-10

Abstract: Abstract Deep neural networks are a powerful tool for characterizing quantum states. Existing networks are typically trained with experimental data gathered from the quantum state that needs to be characterized. But is it possible to train a neural network offline, on a different set of states? Here we introduce a network that can be trained with classically simulated data from a fiducial set of states and measurements, and can later be used to characterize quantum states that share structural similarities with the fiducial states. With little guidance of quantum physics, the network builds its own data-driven representation of a quantum state, and then uses it to predict the outcome statistics of quantum measurements that have not been performed yet. The state representations produced by the network can also be used for tasks beyond the prediction of outcome statistics, including clustering of quantum states and identification of different phases of matter.

Date: 2022
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DOI: 10.1038/s41467-022-33928-z

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