Flexible learning of quantum states with generative query neural networks

Yan Zhu; Ya-Dong Wu; Ge Bai; Dong-Sheng Wang; Yuexuan Wang; Giulio Chiribella

doi:10.1038/s41467-022-33928-z

Nature Communications (Oct 2022)

Flexible learning of quantum states with generative query neural networks

Yan Zhu,
Ya-Dong Wu,
Ge Bai,
Dong-Sheng Wang,
Yuexuan Wang,
Giulio Chiribella

Affiliations

Yan Zhu: QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong
Ya-Dong Wu: QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong
Ge Bai: QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong
Dong-Sheng Wang: CAS Key Laboratory of Theoretical Physics, Institute of Theoretical Physics, Chinese Academy of Sciences
Yuexuan Wang: QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong
Giulio Chiribella: QICI Quantum Information and Computation Initiative, Department of Computer Science, The University of Hong Kong

DOI: https://doi.org/10.1038/s41467-022-33928-z
Journal volume & issue: Vol. 13, no. 1
pp. 1 – 10

Abstract

Read online

The use of machine learning to characterise quantum states has been demonstrated, but usually training the algorithm using data from the same state one wants to characterise. Here, the authors show an algorithm that can learn all states that share structural similarities with the ones used for the training.

Published in Nature Communications

ISSN: 2041-1723 (Online)
Publisher: Nature Portfolio
Country of publisher: United Kingdom
LCC subjects: Science
Website: https://www.nature.com/ncomms/

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