Neural-network quantum state tomography
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F22%3A73613312" target="_blank" >RIV/61989592:15310/22:73613312 - isvavai.cz</a>
Result on the web
<a href="https://journals.aps.org/pra/pdf/10.1103/PhysRevA.106.012409" target="_blank" >https://journals.aps.org/pra/pdf/10.1103/PhysRevA.106.012409</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1103/PhysRevA.106.012409" target="_blank" >10.1103/PhysRevA.106.012409</a>
Alternative languages
Result language
angličtina
Original language name
Neural-network quantum state tomography
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10306 - Optics (including laser optics and quantum optics)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
PHYSICAL REVIEW A
ISSN
2469-9926
e-ISSN
2469-9934
Volume of the periodical
106
Issue of the periodical within the volume
1
Country of publishing house
US - UNITED STATES
Number of pages
8
Pages from-to
"012409-1"-"012409-8"
UT code for WoS article
000824587200013
EID of the result in the Scopus database
2-s2.0-85134202241