Numerical optimization of a nanophotonic cavity by machine learning for near-unity photon indistinguishability at room temperature
J. Guimbao, L. Sanchis, L.M. Weituschat, J.M. Llorens, M. Song, J., Cardenas, P.A. Postigo

TL;DR
This paper demonstrates a numerical design and machine learning optimization of a nanophotonic cavity that achieves near-unity photon indistinguishability and high efficiency at room temperature, advancing quantum photonic integration.
Contribution
It introduces a hybrid slot-Bragg nanocavity design optimized via machine learning to enhance photon indistinguishability and coupling efficiency at room temperature.
Findings
Achieves near-unity photon indistinguishability (I) and high coupling efficiency ({eta}) at RT.
Reduces modal volume by nearly three times through machine learning optimization.
Provides a design compatible with current fabrication technology and nanometer-level emitter positioning.
Abstract
Room-temperature (RT), on-chip deterministic generation of indistinguishable photons coupled to photonic integrated circuits is key for quantum photonic applications. Nevertheless, high indistinguishability (I) at RT is difficult to obtain due to the intrinsic dephasing of most deterministic single-photon sources (SPS). Here we present a numerical demonstration of the design and optimization of a hybrid slot-Bragg nanophotonic cavity that achieves theoretical near-unity I and high coupling efficiency (\b{eta}) at RT for a variety of singlephoton emitters. Our numerical simulations predict modal volumes in the order of 10-3 ({\lambda}/2n)3 , allowing for strong coupling of quantum photonic emitters that can be heterogeneously integrated. We show that high I and \b{eta} should be possible by fine-tuning the quality factor (Q) depending on the intrinsic properties of the single-photon…
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Taxonomy
TopicsPhotonic and Optical Devices · Advanced Fiber Laser Technologies · Neural Networks and Reservoir Computing
