Chunhui Jiang | Searcharxiv

arXiv2025

Strain Enhanced Spin Readout Contrast in Silicon Carbide Membranes

Quantum defects in solids have emerged as a transformative platform for advancing quantum technologies. A key requirement for these applications is achieving high-fidelity single-spin readout, particularly at room temperature for quantum biosensing. Here, we demonstrate through ab initio simulations of a primary quantum defect in 4H silicon carbide that strain is an effective control parameter for significantly enhancing readout contrast. We validate this principle experimentally by inducing local strain in silicon carbide-on-insulator membranes, achieving a readout contrast exceeding 60% while preserving the favorable coherence properties of single spins. Our findings establish strain engineering as a powerful and versatile strategy for optimizing coherent spin-photon interfaces in solid-state quantum systems.

quant-ph↗

arXiv2017

Relief R-CNN : Utilizing Convolutional Features for Fast Object Detection

R-CNN style methods are sorts of the state-of-the-art object detection methods, which consist of region proposal generation and deep CNN classification. However, the proposal generation phase in this paradigm is usually time consuming, which would slow down the whole detection time in testing. This paper suggests that the value discrepancies among features in deep convolutional feature maps contain plenty of useful spatial information, and proposes a simple approach to extract the information for fast region proposal generation in testing. The proposed method, namely Relief R-CNN (R2-CNN), adopts a novel region proposal generator in a trained R-CNN style model. The new generator directly generates proposals from convolutional features by some simple rules, thus resulting in a much faster proposal generation speed and a lower demand of computation resources. Empirical studies show that R2-CNN could achieve the fastest detection speed with comparable accuracy among all the compared algorithms in testing.

cs.CV↗