Harnessing electro-optic correlations in an efficient mechanical converter

By Andrew Higginbotham, P. S. Burns, M. Urmey, R. W. Peterson, N. Kampel, B. Brubaker, Graeme Smith, Konrad W. Lehnert, C. Regal. Nature Physics, 2017

246 citations recorded by the source corpus.

An optical network of superconducting quantum bits (qubits) is an appealing platform for quantum communication and distributed quantum computing, but developing a quantum-compatible link between the microwave and optical domains remains an outstanding challenge. Operating at T < 100 mK temperatures, as required for quantum electrical circuits, we demonstrate a mechanically mediated microwave–optical converter with 47% conversion efficiency, and use a classical feed-forward protocol to reduce added noise to 38 photons. The feed-forward protocol harnesses our discovery that noise emitted from the two converter output ports is strongly correlated because both outputs record thermal motion of the same mechanical mode. We also discuss a quantum feed-forward protocol that, given high system efficiencies, would allow quantum information to be transferred even when thermal phonons enter the mechanical element faster than the electro-optic conversion rate.A mechanical-mediated quantum-compatible microwave–optical converter achieves high efficiency through a feed-forward protocol that harnesses correlations in the output noise.

Subfields: Artificial Intelligence, Atomic and Molecular Physics, and Optics, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/53971f6f59423e1dd12d66f62c66c48317d32a93

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In Artificial Intelligence

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  2. Alexandre Blais
  3. Franco Nori
  4. Andreas Wallraff
  5. A. Grimsmo
  6. Christopher R. Monroe
  7. Dirk Englund
  8. Mikhail D. Lukin

In Atomic and Molecular Physics, and Optics

  1. Steven M. Girvin
  2. Alexandre Blais
  3. Franco Nori
  4. Andreas Wallraff
  5. Mikhail D. Lukin
  6. Dirk Englund
  7. Can-Ming Hu
  8. A. Grimsmo

In Electrical and Electronic Engineering

  1. Dirk Englund
  2. Marko Loncar
  3. Jelena Vuckovic
  4. Mikhail D. Lukin
  5. Edo Waks
  6. A. Grimsmo
  7. A. Safavi-Naeini
  8. Can-Ming Hu

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