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Quantifying entanglement in a 68-billion-dimensional quantum state space

Author

Listed:
  • James Schneeloch

    (Information Directorate)

  • Christopher C. Tison

    (Information Directorate
    Florida Atlantic University
    Quanterion Solutions Incorporated)

  • Michael L. Fanto

    (Information Directorate
    Rochester Institute of Technology)

  • Paul M. Alsing

    (Information Directorate)

  • Gregory A. Howland

    (Information Directorate
    Rochester Institute of Technology)

Abstract

Entanglement is the powerful and enigmatic resource central to quantum information processing, which promises capabilities in computing, simulation, secure communication, and metrology beyond what is possible for classical devices. Exactly quantifying the entanglement of an unknown system requires completely determining its quantum state, a task which demands an intractable number of measurements even for modestly-sized systems. Here we demonstrate a method for rigorously quantifying high-dimensional entanglement from extremely limited data. We improve an entropic, quantitative entanglement witness to operate directly on compressed experimental data acquired via an adaptive, multilevel sampling procedure. Only 6,456 measurements are needed to certify an entanglement-of-formation of 7.11 ± .04 ebits shared by two spatially-entangled photons. With a Hilbert space exceeding 68 billion dimensions, we need 20-million-times fewer measurements than the uncompressed approach and 1018-times fewer measurements than tomography. Our technique offers a universal method for quantifying entanglement in any large quantum system shared by two parties.

Suggested Citation

  • James Schneeloch & Christopher C. Tison & Michael L. Fanto & Paul M. Alsing & Gregory A. Howland, 2019. "Quantifying entanglement in a 68-billion-dimensional quantum state space," Nature Communications, Nature, vol. 10(1), pages 1-7, December.
    • Handle: RePEc:nat:natcom:v:10:y:2019:i:1:d:10.1038_s41467-019-10810-z
    DOI: 10.1038/s41467-019-10810-z
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