arXiv:2404.11193 · Machine Learning Science and Technology
Photonic indistinguishability characterization and optimization for cavity-based single-photon source
Miao Cai, Mingyuan Chen, Jiang-Shan Tang, Keyu Xia
Abstract
Abstract Indistinguishability of single photons from independent sources is critically important for scalable quantum technologies. We provide a comprehensive comparison of single-photon indistinguishability of different kinds of cavity quantum electrodynamics (CQEDs) systems by numerically simulating Hong–Ou–Mandel two-photon interference. We find that the CQED system using natural atoms exhibits superiority in indistinguishability, benefiting from the inherently identical features. Moreover, a Λ − type three-level atom shows essential robustness against variation of various system parameters because it exploits the two ground states with considerably smaller decay rates for single-photon generation. Furthermore, a machine learning-based framework is proposed to significantly and robustly improve single-photon indistinguishability for two non-identical CQED systems. This work may pave the way for designing and engineering reliable and scalable photon-based quantum technologies.