Sarah Wang | Searcharxiv

arXiv2024

Highly Efficient Superconducting Diodes and Rectifiers for Quantum Circuitry

Superconducting electronics is essential for energy-efficient quantum and classical high-end computing applications. Towards this goal, non-reciprocal superconducting circuit elements, such as superconducting diodes (SDs) can fulfill many critical needs. SDs have been the subject of multiple studies, but integrating several SDs in a superconducting circuit remains a challenge. Here we implement the first SD bridge with multiple SDs exhibiting reproducible characteristics operating at temperatures of a few Kelvin. We demonstrate its functionality as a full wave rectifier using elemental superconductors and insulating ferromagnets, with efficiency up to 43%, and ac to dc signal conversion capabilities at frequencies up to 40 kHz. Our results show a pathway with a highly scalable thin film platform for nonreciprocal superconducting circuits. They could significantly reduce energy consumption as well as decohering thermal and electromagnetic noise in quantum computing.

cond-mat.supr-con↗

arXiv2021

OpenRooms: An End-to-End Open Framework for Photorealistic Indoor Scene Datasets

We propose a novel framework for creating large-scale photorealistic datasets of indoor scenes, with ground truth geometry, material, lighting and semantics. Our goal is to make the dataset creation process widely accessible, transforming scans into photorealistic datasets with high-quality ground truth for appearance, layout, semantic labels, high quality spatially-varying BRDF and complex lighting, including direct, indirect and visibility components. This enables important applications in inverse rendering, scene understanding and robotics. We show that deep networks trained on the proposed dataset achieve competitive performance for shape, material and lighting estimation on real images, enabling photorealistic augmented reality applications, such as object insertion and material editing. We also show our semantic labels may be used for segmentation and multi-task learning. Finally, we demonstrate that our framework may also be integrated with physics engines, to create virtual robotics environments with unique ground truth such as friction coefficients and correspondence to real scenes. The dataset and all the tools to create such datasets will be made publicly available.

cs.CV↗