RT Journal Article
T1 Neural-network quantum state tomography
A1 Koutný, Dominik
A1 Motka, Libor
A1 Hradil, Zdeněk
A1 Řeháček, Jaroslav
A1 Sánchez Soto, Luis Lorenzo
AB 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.
PB Amer Physical Soc
SN 2469-9926
YR 2022
FD 2022-07-06
LK https://hdl.handle.net/20.500.14352/71858
UL https://hdl.handle.net/20.500.14352/71858
LA eng
NO © 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.
NO Unión Europea. Horizonte 2020
NO Ministerio de Ciencia e Innovación (MICINN)
NO Palacky University
DS Docta Complutense
RD 10 abr 2025