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Research Highlight

Non-Gaussian photonic state engineering with the quantum frequency processor

Topics:
Setup explored for non-Gaussian state preparation with the quantum frequency processor, or QFP. The undetected mode is left in state |Φ⟩. CSED Computational Sciences and Engineering Division ORNL
Setup explored for non-Gaussian state preparation with the quantum frequency processor, or QFP. The undetected mode is left in state |Φ⟩.

The Science
A team led by ORNL scientists in collaboration with University of Arizona researchers developed a new approach to engineer non-Gaussian photonic states in discrete frequency bins. This approach combines quantum frequency processing (QFP) with photon number-resolving detection. Simulated examples demonstrate the potential to produce high-fidelity Schrödinger cat states with a reasonable amount of existing resources.

The Impact

  • Non-Gaussian quantum states of light are critical resources for optical quantum repeaters, but efficient methods to generate these states so far remain elusive.
  • This study lays out a mathematical framework to produce complex quantum states in frequency bins for the first time. 
  • Results pave the way for single-spatial-mode, fiber-optic-compatible non-Gaussian quantum states.
  • Next steps include factoring in loss, designing GKP qubits, and experimental demonstration.

PI/Facility Lead: Joseph M. Lukens and Nicholas A. Peters

Funding: DOE Transparent Optical Quantum Networks for Distributed Science Program, Early Career Research Program, ASCR
Publication for this work:

Team Members: Andrew J. Pizzimenti (ORNL), Joseph M. Lukens (ORNL), Hsuan-Hao Lu (Purdue University), Nicholas A. Peters (ORNL), Saikat Guha (University of Arizona), Christos Gagatsos (University of Arizona)

Summary: This work develops an approach for engineering non-Gaussian photonic states in discrete frequency bins. Combining the quantum frequency processor and photon number-resolving detection, simulated examples demonstrate the potential for producing high-fidelity cat states with reasonable resource requirements.

Acknowledgement of support: This work was funded in part by the DOE Office of Science, Advanced Scientific Computing Research (ASCR) program.