Machine-Learning-Assisted Many-Body Entanglement Measurement

Johnnie Gray, Leonardo Banchi, Abolfazl Bayat, and Sougato Bose
Phys. Rev. Lett. 121, 150503 – Published 12 October 2018
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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 NA and NB, with O(NA+NB) 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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  • Received 20 March 2018

DOI:https://doi.org/10.1103/PhysRevLett.121.150503

© 2018 American Physical Society

Physics Subject Headings (PhySH)

Condensed Matter, Materials & Applied PhysicsQuantum Information, Science & TechnologyInterdisciplinary PhysicsGeneral Physics

Authors & Affiliations

Johnnie Gray1,*, Leonardo Banchi1, Abolfazl Bayat2,1, and Sougato Bose1

  • 1Department of Physics and Astronomy, University College London, Gower Street, London WC1E 6BT, United Kingdom
  • 2Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu 610051, China

  • *john.gray.14@ucl.ac.uk

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Issue

Vol. 121, Iss. 15 — 12 October 2018

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