A hybrid waveguide scheme for silicon-based quantum photonic circuits with quantum light sources
Lingjie Yu, Chenzhi Yuan, Renduo Qi Yidong Huang, Wei Zhang

TL;DR
This paper introduces a hybrid silicon waveguide design that minimizes noise photons in quantum photonic circuits, enhancing the performance of quantum light sources through spectral separation and structural innovation.
Contribution
A novel hybrid waveguide scheme combining strip and shallow-ridge structures to suppress noise photons in silicon-based quantum photonic circuits.
Findings
The scheme effectively isolates desired photon pairs in the strip waveguide.
The shallow-ridge waveguide can be used for linear operations and quantum state manipulation.
Theoretical analysis and initial experiments support the scheme's feasibility.
Abstract
We propose a hybrid silicon waveguide scheme to avoid the impact of noise photons induced by pump lights in application scenarios of quantum photonic circuits with quantum light sources. The scheme is composed of strip waveguide and shallow-ridge waveguide structures. It utilizes the difference of biphoton spectra generated by spontaneous four wave mixing (SFWM) in these two waveguides. By proper pumping setting and signal/idler wavelength selection, the generation of desired photon pairs is confined in the strip waveguide. The impact of noise photons generated by SFWM in the shallow-ridge waveguide could be avoided. Hence, the shallow-ridge waveguide could be used to realize various linear operation devices for pump light and quantum state manipulations. The feasibility of this scheme is verified by theoretical analysis and primary experiment. Two applications are proposed and…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsPhotonic and Optical Devices · Optical Network Technologies · Neural Networks and Reservoir Computing
