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Status: Unchecked ×Keyword: undersampled measurements ×Clear all

3 claims from 1 paper

1. Computer Science › Quantum Information and Cryptography

   Deep learning of quantum entanglement from incomplete measurements

   Koutný, Ginés, Moczała-Dusanowska et al. · Science Advances · 2023

   The paper shows neural networks can quantify entanglement from an incomplete set of local measurements, without knowing the full quantum state, and trained only on simulated data.

   Unchecked3 claims
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   1. UncheckedUsing neural networks on undersampled measurements, the authors report entanglement quantification errors up to ten times lower than state-of-the-art quantum tomography.“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.”
   2. UncheckedThe neural networks that quantify entanglement from incomplete measurements were trained only on simulated data, not on real experimental data.“Furthermore, we achieve this result employing networks trained using exclusively simulated data.”
   3. UncheckedThe authors derive a convolutional-network method that accepts data from various measurement setups and works, to some extent, independently of the measuring device.“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.”