Deep learning of quantum entanglement from incomplete measurements

• URL: http://arxiv.org/abs/2205.01462v6
• Date: Mon, 24 Jul 2023 08:31:58 GMT
• Title: Deep learning of quantum entanglement from incomplete measurements
• Authors: Dominik Koutn\'y, Laia Gin\'es, Magdalena Mocza{\l}a-Dusanowska, Sven H\"ofling, Christian Schneider, Ana Predojevi\'c, Miroslav Je\v{z}ek
• Abstract summary: We demonstrate that by employing neural networks we can quantify the degree of entanglement without needing 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. 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.
• Score: 0.2493740042317776
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The quantification of the entanglement present in a physical system is of para\-mount importance for fundamental research and many cutting-edge applications. Currently, 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 employing neural networks we can quantify the degree of entanglement without needing 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 employing networks trained using exclusively simulated data. Finally, 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.

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