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Machine-Learning-Assisted Many-Body Entanglement Measurement

Johnnie GrayDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United KingdomLeonardo BanchiDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United KingdomAbolfazl BayatDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United KingdomSougato BoseDepartment of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom

2018en

ABI

Annotatsiya

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 O(N_{A}+N_{B}) measurements, and without any prior knowledge of the state. The method exploits a neural network to learn the unknown, nonlinear 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 nonequilibrium regimes.

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DOI: 10.1103/physrevlett.121.150503

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