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Showing 1–5 of 5 results
Advanced filters: Author: Seou Choi Clear advanced filters

  • Characterising an optical quantum state confined in a cavity is not an easy task, as standard tomographic techniques works by interfering propagating fields and therefore encounters the problems relative to outcoupling the state. Here, the authors fill this gap for states generated within a nonlinear cavity featuring multiple steady states.

    • Seou Choi
    • Yannick Salamin
    • Marin Soljačić
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-8

  • Scintillators are used for converting X-ray energy into visible light in imaging technologies. Here, the authors present a scalable fabrication approach for large-area nanostructured scintillators, and achieve six-fold enhancement in light yield compared to unpatterned scintillators.

    • Louis Martin-Monier
    • Simo Pajovic
    • Charles Roques-Carmes
    ResearchOpen Access
    Nature Communications
    Volume: 16, P: 1-8

  • Probabilistic machine learning is an emerging computing paradigm which utilizes controllable random sources to encode uncertainty and enable statistical modelling. Here, authors harness quantum vacuum noise as a controllable random source to perform probabilistic inference and image generation.

    • Seou Choi
    • Yannick Salamin
    • Marin Soljačić
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-8

  • Optical computing often employs tailor-made hardware to implement specific algorithms, trading generality for improved performance in key aspects like speed and power efficiency. We propose an experimentally viable photonic approach to solve arbitrary probabilistic computing problems, used e.g. for solving difficult combinatorial optimization problems.

    • Michael Horodynski
    • Charles Roques-Carmes
    • Marin Soljačić
    ResearchOpen Access
    Communications Physics
    Volume: 8, P: 1-6