Search for: All records

Deterministic positioning single site-controlled high symmetric InGaAs quantum dots (QDs) in (111)B-oriented GaAs photonic crystal cavities with nanometer-scale accuracy provides an idea component for building integrated quantum photonic circuits. However, it has been a long-standing challenge of improving cavityQ-factors in such systems. Here, by optimizing the trade-off between the cavity loss and QD spectral quality, we demonstrate our site-controlled QD-nanocavity system operating in the intermediate coupling regime mediated by phonon scattering, with the dynamic coexistence of strong and weak coupling. The cavity-exciton detuning-dependent micro-photoluminescence spectrum reveals concurrence of a trend of exciton-polariton mode avoided crossing, as a signature of Rabi doublet of the strongly coupled system. Meanwhile, a trend of keeping constant or slight blue shift of coupled exciton–cavity mode(CM) energy across zero-detuning is ascribed to the formation of collective states mediated by phonon-assisted coupling, and their rare partial out-of-synchronization linewidth-narrowing is linked to their coexisting strong-weak coupling regime. We further reveal the pump power-dependent anti-bunching photon statistical dynamics of this coexisting strong-weak coupled system and the optical features of strongly confined exciton-polaritons, and dark-exciton-like states. These observations demonstrate the potential capabilities of site-controlled QD-cavity systems as deterministic quantum nodes for on-chip quantum information processing and provide guidelines for future device optimization for achieving the strong coupling regime.

 

We present MultiBodySync, a novel, end-to-end trainable multi-body motion segmentation and rigid registration framework for multiple input 3D point clouds. The two non-trivial challenges posed by this multi-scan multibody setting that we investigate are: (i) guaranteeing correspondence and segmentation consistency across multiple input point clouds capturing different spatial arrangements of bodies or body parts; and (ii) obtaining robust motion-based rigid body segmentation applicable to novel object categories. We propose an approach to address these issues that incorporates spectral synchronization into an iterative deep declarative network, so as to simultaneously recover consistent correspondences as well as motion segmentation. At the same time, by explicitly disentangling the correspondence and motion segmentation estimation modules, we achieve strong generalizability across different object categories. Our extensive evaluations demonstrate that our method is effective on various datasets ranging from rigid parts in articulated objects to individually moving objects in a 3D scene, be it single-view or full point clouds.