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Neural-network quantum state tomography



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Koutný, Dominik and Motka, Libor and Hradil, Zdeněk and Řeháček, Jaroslav and Sánchez Soto, Luis Lorenzo (2022) Neural-network quantum state tomography. Physical review A, 106 (1). ISSN 2469-9926

[thumbnail of SanchezSotoLL 48 LIBRE.pdf]

Official URL: http://dx.doi.org/10.1103/PhysRevA.106.012409


We revisit the application of neural networks to quantum state tomography. We confirm that the positivity constraint can be successfully implemented with trained networks that convert outputs from standard feedforward neural networks to valid descriptions of quantum states. Any standard neural-network architecture can be adapted with our method. Our results open possibilities to use state-of-the-art deep-learning methods for quantum state reconstruction under various types of noise.

Item Type:Article
Additional Information:

© 2022 American Physical Society.
The authors thank Miroslav Ježek for useful discussions and two anonymous reviewers for their constructive and detailed comments. This work was supported by the European Union’s Horizon 2020 Research and Innovation Programme under the QuantERA Programme through the project ApresSF and from the EU Grant No. 899587 (Project Stormytune), the Palacký University Grant No. IGA_PrF_2021_002, and the Spanish Ministerio de Ciencia e Innovacion Grant No.

Uncontrolled Keywords:Optics; Physics; Atomic; Molecular; Chemical
Subjects:Sciences > Physics > Optics
ID Code:74062
Deposited On:03 Aug 2022 14:50
Last Modified:04 Aug 2022 07:19

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