Entanglement quantification from collective measurements processed by machine learning

• URL: http://arxiv.org/abs/2203.01607v1
• Date: Thu, 3 Mar 2022 10:03:57 GMT
• Title: Entanglement quantification from collective measurements processed by machine learning
• Authors: Jan Roik, Karol Bartkiewicz, Anton\'in \v{C}ernoch, and Karel Lemr
• Abstract summary: Instead of analytical formulae, we employ artificial neural networks to predict the amount of entanglement in a quantum state. For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we investigate how to reduce the number of measurement configurations needed for sufficiently precise entanglement quantification. Instead of analytical formulae, we employ artificial neural networks to predict the amount of entanglement in a quantum state based on results of collective measurements (simultaneous measurements on multiple instances of the investigated state). This approach allows us to explore the precision of entanglement quantification as a function of measurement configurations. For the purpose of our research, we consider general two-qubit states and their negativity as entanglement quantifier. We outline the benefits of this approach in future quantum communication networks.

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