Matches in SemOpenAlex for { <https://semopenalex.org/work/W2897050953> ?p ?o ?g. }
- W2897050953 abstract "Entanglement not only plays a crucial role in quantum technologies, but is key to our understanding of quantum correlations in many-body systems. However, in an experiment, the only way of measuring entanglement in a generic mixed state is through reconstructive quantum tomography, requiring an exponential number of measurements in the system size. Here, we propose a machine learning assisted scheme to measure the entanglement between arbitrary subsystems of size $N_A$ and $N_B$, with $mathcal{O}(N_A + N_B)$ measurements, and without any prior knowledge of the state. The method exploits a neural network to learn the unknown, non-linear function relating certain measurable moments and the logarithmic negativity. Our procedure will allow entanglement measurements in a wide variety of systems, including strongly interacting many body systems in both equilibrium and non-equilibrium regimes." @default.
- W2897050953 created "2018-10-26" @default.
- W2897050953 creator A5020091882 @default.
- W2897050953 creator A5044565348 @default.
- W2897050953 creator A5060603833 @default.
- W2897050953 creator A5069913087 @default.
- W2897050953 date "2018-10-12" @default.
- W2897050953 modified "2023-10-16" @default.
- W2897050953 title "Machine-Learning-Assisted Many-Body Entanglement Measurement" @default.
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- W2897050953 doi "https://doi.org/10.1103/physrevlett.121.150503" @default.
- W2897050953 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/30362777" @default.
- W2897050953 hasPublicationYear "2018" @default.
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