4.8 Article

Experimental quantum compressed sensing for a seven-qubit system

期刊

NATURE COMMUNICATIONS
卷 8, 期 -, 页码 -

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NATURE PUBLISHING GROUP
DOI: 10.1038/ncomms15305

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资金

  1. Templeton Foundation
  2. EU (RAQUEL)
  3. EU (AQuS)
  4. ERC (TAQ)
  5. Freie Universitat Berlin
  6. University of Cologne within the Excellence Initiative of the German Federal and State Governments
  7. DFG [SPP 1798 CoSIP, EI 519/7-1, EI 519/9-1, CRC 183, GRO 4334/2-1]
  8. Austrian Science Fund (FWF) through the SFB FoQus (FWF) [F4002-N16]
  9. Institut fur Quanteninformation GmbH
  10. BMBF (Q.com)
  11. Universities Australia
  12. DAAD (German Federal Ministry of Education and Research)
  13. Australian Research Council via EQuS project [CE11001013]
  14. US Army Research Office within QCVV program [W911NF-14-1-0098, W911NF-16-1-0070, W911NF-14-1-0103]
  15. Office of the Director of National Intelligence (ODNI), Intelligence Advanced Research Projects Activity (IARPA) through Army Research Office [W911NF-10-1-0284]
  16. Australian Research Council Future Fellowship [FT130101744]

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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.

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