Deep learning of quantum entanglement from incomplete measurements

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989592%3A15310%2F23%3A73619529" target="_blank" >RIV/61989592:15310/23:73619529 - isvavai.cz</a>
Result on the web
<a href="https://www.science.org/doi/10.1126/sciadv.add7131" target="_blank" >https://www.science.org/doi/10.1126/sciadv.add7131</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1126/sciadv.add7131" target="_blank" >10.1126/sciadv.add7131</a>

Result language
angličtina
Original language name
Deep learning of quantum entanglement from incomplete measurements
Original language description
The quantification of the entanglement present in a physical system is of paramount importance for fundamental research and many cutting-edge applications. Now, achieving this goal requires either a priori knowledge on the system or very demanding experimental procedures such as full state tomography or collective measurements. Here, we demonstrate that, by using neural networks, we can quantify the degree of entanglement without the need to know the full description of the quantum state. Our method allows for direct quantification of the quantum correlations using an incomplete set of local measurements. Despite using undersampled measurements, we achieve a quantification error of up to an order of magnitude lower than the state-of-the-art quantum tomography. Furthermore, we achieve this result using networks trained using exclusively simulated data. Last, we derive a method based on a convolutional network input that can accept data from various measurement scenarios and perform, to some extent, independently of the measurement device.
Czech name
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Czech description
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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)

Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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