Deterministic delivery of remote entanglement on a quantum network

By Peter Conway Humphreys, N. Kalb, Jaco P. J. Morits, R. Schouten, R. Vermeulen, Daniel J. Twitchen, M. Markham, Ronald Hanson. Nature, 2017

593 citations recorded by the source corpus.

Large-scale quantum networks promise to enable secure communication, distributed quantum computing, enhanced sensing and fundamental tests of quantum mechanics through the distribution of entanglement across nodes1–7. Moving beyond current two-node networks8–13 requires the rate of entanglement generation between nodes to exceed the decoherence (loss) rate of the entanglement. If this criterion is met, intrinsically probabilistic entangling protocols can be used to provide deterministic remote entanglement at pre-specified times. Here we demonstrate this using diamond spin qubit nodes separated by two metres. We realize a fully heralded single-photon entanglement protocol that achieves entangling rates of up to 39 hertz, three orders of magnitude higher than previously demonstrated two-photon protocols on this platform14. At the same time, we suppress the decoherence rate of remote-entangled states to five hertz through dynamical decoupling. By combining these results with efficient charge-state control and mitigation of spectral diffusion, we deterministically deliver a fresh remote state with an average entanglement fidelity of more than 0.5 at every clock cycle of about 100 milliseconds without any pre- or post-selection. These results demonstrate a key building block for extended quantum networks and open the door to entanglement distribution across multiple remote nodes. Remote deterministic spin–spin entanglement is achieved using nitrogen–vacancy centres in diamonds and a single-photon entangling protocol, with much improved entangling rates compared to previously used two-photon protocols.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics

View Paper: https://www.semanticscholar.org/paper/c415db3e58ccdd79458c6e5ea5261f659a9c031f

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In Artificial Intelligence

  1. Mark Saffman
  2. Mikhail D. Lukin
  3. E. Demler
  4. Z. Nazario
  5. Christopher R. Monroe
  6. V. Vuleti'c
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In Atomic and Molecular Physics, and Optics

  1. Mark Saffman
  2. I. Bloch
  3. Mikhail D. Lukin
  4. E. Demler
  5. M. Zwierlein
  6. Lincoln D. Carr
  7. Ian B. Spielman
  8. Christopher R. Monroe

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