4.8 Article

Semidefinite Tests for Quantum Network Topologies

期刊

PHYSICAL REVIEW LETTERS
卷 125, 期 11, 页码 -

出版社

AMER PHYSICAL SOC
DOI: 10.1103/PhysRevLett.125.110505

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资金

  1. Excellence Initiative of the German Federal and State Governments [ZUK 81]
  2. ARO [W911NF-14-1-0098]
  3. DFG [SPP1798 CoSIP]
  4. Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany's Excellence StrategyCluster of Excellence Matter and Light for Quantum Computing (ML4Q) [EXC 2004/1-390534769]
  5. John Templeton Foundation [61084]
  6. Serrapilheira Institute [Serra-1708-15763]
  7. Brazilian National Council for Scientific and Technological Development (CNPq) via the National Institute for Science and Technology on Quantum Information (INCT-IQ)
  8. Ministry of Science, Technology, Innovation and Communication [307172/2017-1, 406574/2018-9]
  9. MEC [307172/2017-1, 406574/2018-9]
  10. Grand Challenges Initiative at Chapman University
  11. Foundational Questions Institute [FQXi-RFP-IPW-1905]
  12. Fetzer Franklin Fund, a donor advised fund of Silicon Valley Community Foundation

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Quantum networks play a major role in long-distance communication, quantum cryptography, clock synchronization, and distributed quantum computing. Generally, these protocols involve many independent sources sharing entanglement among distant parties that, upon measuring their systems, generate correlations across the network. The question of which correlations a given quantum network can give rise to remains almost uncharted. Here we show that constraints on the observable covariances, previously derived for the classical case, also hold for quantum networks. The network topology yields tests that can be cast as semidefinite programs, thus allowing for the efficient characterization of the correlations in a wide class of quantum networks, as well as systematic derivations of device-independent and experimentally testable witnesses. We obtain such semidefinite tests for fixed measurement settings, as well as parties that independently choose among collections of measurement settings. The applicability of the method is demonstrated for various networks, and compared with previous approaches.

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