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Experimental quantum compressed sensing for a seven-qubit system

Author

Listed:
  • C. A. Riofrío

    (Dahlem Center for Complex Quantum Systems, Freie Universität Berlin)

  • D. Gross

    (Institute for Theoretical Physics, University of Cologne
    Centre for Engineered Quantum Systems, School of Physics, The University of Sydney)

  • S. T. Flammia

    (Centre for Engineered Quantum Systems, School of Physics, The University of Sydney)

  • T. Monz

    (Institut für Experimentalphysik, Universität Innsbruck)

  • D. Nigg

    (Institut für Experimentalphysik, Universität Innsbruck)

  • R. Blatt

    (Institut für Experimentalphysik, Universität Innsbruck
    Institut für Quantenoptik und Quanteninformation, Österreichische Akademie der Wissenschaften)

  • J. Eisert

    (Dahlem Center for Complex Quantum Systems, Freie Universität Berlin)

Abstract

Well-controlled quantum devices with their increasing system size face a new roadblock hindering further development of quantum technologies. The effort of quantum tomography—the reconstruction of states and processes of a quantum device—scales unfavourably: state-of-the-art systems can no longer be characterized. Quantum compressed sensing mitigates this problem by reconstructing states from incomplete data. Here we present an experimental implementation of compressed tomography of a seven-qubit system—a topological colour code prepared in a trapped ion architecture. We are in the highly incomplete—127 Pauli basis measurement settings—and highly noisy—100 repetitions each—regime. Originally, compressed sensing was advocated for states with few non-zero eigenvalues. We argue that low-rank estimates are appropriate in general since statistical noise enables reliable reconstruction of only the leading eigenvectors. The remaining eigenvectors behave consistently with a random-matrix model that carries no information about the true state.

Suggested Citation

  • C. A. Riofrío & D. Gross & S. T. Flammia & T. Monz & D. Nigg & R. Blatt & J. Eisert, 2017. "Experimental quantum compressed sensing for a seven-qubit system," Nature Communications, Nature, vol. 8(1), pages 1-8, August.
    • Handle: RePEc:nat:natcom:v:8:y:2017:i:1:d:10.1038_ncomms15305
    DOI: 10.1038/ncomms15305
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    Cited by:

    1. Das, Laya & Garg, Dinesh & Srinivasan, Babji, 2020. "NeuralCompression: A machine learning approach to compress high frequency measurements in smart grid," Applied Energy, Elsevier, vol. 257(C).

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