English

Efficient and broadband quantum frequency comb generation in a monolithic AlGaAs-on-insulator microresonator

Quantum Physics 2026-01-14 v1

Abstract

The exploration of photonic systems for quantum information processing has generated widespread interest in multiple cutting-edge research fields. Photonic frequency encoding stands out as an especially viable approach, given its natural alignment with established optical communication technologies, including fiber networks and wavelength-division multiplexing systems. Substantial reductions in hardware resources and improvements in quantum performance can be expected by utilizing multiple frequency modes. The integration of nonlinear photonics with microresonators provides a compelling way for generating frequency-correlated photon pairs across discrete spectral modes. Here, by leveraging the high material nonlinearity and low nonlinear loss, we demonstrate an efficient chip-scale multi-wavelength quantum light source based on AlGaAs-on-insulator, featuring a free spectral range of approximately 200 GHz at telecom wavelengths. The optimized submicron waveguide geometry provides both high effective nonlinearity (~550 m1^{-1}W1^{-1}) and broad generation bandwidth, producing eleven distinct wavelength pairs across a 35.2 nm bandwidth with an average spectral brightness of 2.64 GHz mW2^{-2}nm1^{-1}. The generation of energy-time entanglement for each pair of frequency modes is verified through Franson interferometry, yielding an average net visibility of 93.1%. With its exceptional optical gain and lasing capabilities, the AlGaAs-on-insulator platform developed here shows outstanding potential for realizing fully integrated, ready-to-deploy quantum photonic systems on chip.

Keywords

Cite

@article{arxiv.2601.08289,
  title  = {Efficient and broadband quantum frequency comb generation in a monolithic AlGaAs-on-insulator microresonator},
  author = {Xiaodong Zheng and Xu Jing and Chenbo Liu and Yufu Li and Runqiu He and Lina Xia and Fei Wang and Yuechan Kong and Tangsheng Chen and Liangliang Lu and Jiayun Dai and Bin Niu},
  journal= {arXiv preprint arXiv:2601.08289},
  year   = {2026}
}

Comments

23 pages, 12 figures