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Arbitrary linear transformations for photons in the frequency synthetic dimension

Author

Listed:
  • Siddharth Buddhiraju

    (Stanford University)

  • Avik Dutt

    (Stanford University)

  • Momchil Minkov

    (Stanford University)

  • Ian A. D. Williamson

    (Stanford University)

  • Shanhui Fan

    (Stanford University)

Abstract

Arbitrary linear transformations are of crucial importance in a plethora of photonic applications spanning classical signal processing, communication systems, quantum information processing and machine learning. Here, we present a photonic architecture to achieve arbitrary linear transformations by harnessing the synthetic frequency dimension of photons. Our structure consists of dynamically modulated micro-ring resonators that implement tunable couplings between multiple frequency modes carried by a single waveguide. By inverse design of these short- and long-range couplings using automatic differentiation, we realize arbitrary scattering matrices in synthetic space between the input and output frequency modes with near-unity fidelity and favorable scaling. We show that the same physical structure can be reconfigured to implement a wide variety of manipulations including single-frequency conversion, nonreciprocal frequency translations, and unitary as well as non-unitary transformations. Our approach enables compact, scalable and reconfigurable integrated photonic architectures to achieve arbitrary linear transformations in both the classical and quantum domains using current state-of-the-art technology.

Suggested Citation

  • Siddharth Buddhiraju & Avik Dutt & Momchil Minkov & Ian A. D. Williamson & Shanhui Fan, 2021. "Arbitrary linear transformations for photons in the frequency synthetic dimension," Nature Communications, Nature, vol. 12(1), pages 1-9, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-22670-7
    DOI: 10.1038/s41467-021-22670-7
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    Cited by:

    1. Jérôme Sol & David R. Smith & Philipp Hougne, 2022. "Meta-programmable analog differentiator," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Han Zhao & Bingzhao Li & Huan Li & Mo Li, 2022. "Enabling scalable optical computing in synthetic frequency dimension using integrated cavity acousto-optics," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    3. Steven Becker & Dirk Englund & Birgit Stiller, 2024. "An optoacoustic field-programmable perceptron for recurrent neural networks," Nature Communications, Nature, vol. 15(1), pages 1-8, December.
    4. Avik Dutt & Luqi Yuan & Ki Youl Yang & Kai Wang & Siddharth Buddhiraju & Jelena Vučković & Shanhui Fan, 2022. "Creating boundaries along a synthetic frequency dimension," Nature Communications, Nature, vol. 13(1), pages 1-8, December.

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