Deep learning of quantum entanglement from incomplete measurements

Author:

Koutný Dominik1ORCID,Ginés Laia2ORCID,Moczała-Dusanowska Magdalena3ORCID,Höfling Sven4ORCID,Schneider Christian5,Predojević Ana2ORCID,Ježek Miroslav1ORCID

Affiliation:

1. Department of Optics, Faculty of Science, Palacký University, 17. listopadu 12, 77146 Olomouc, Czechia.

2. Department of Physics, Stockholm University, 10691 Stockholm, Sweden.

3. Princeton Institute of Materials, Princeton University, Princeton, NJ 08544, USA.

4. Technische Physik, Physikalisches Institut and Würzburg-Dresden Cluster of Excellence ct.qmat, Universität Würzburg, Am Hubland, D-97074 Würzburg, Germany.

5. Institute of Physics, University of Oldenburg, D-26129 Oldenburg, Germany.

Abstract

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.

Publisher

American Association for the Advancement of Science (AAAS)

Subject

Multidisciplinary

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