Search

Optical cavities can provide fast and non-destructive readout of individual atomic qubits; however, scaling up to many qubits remains a challenge. Using locally addressed excited-state Stark shifts to tune atoms out of resonance, we realize site-selective hyperfine-state cavity readout across a 10-site array. The state discrimination fidelity is 0.994(1) for one atom and 0.989(2) averaged over the entire array at a survival probability of 0.975(1). To further speed up array readout, we demonstrate adaptive search strategies utilizing global/subset checks. Finally, we demonstrate repeated rounds of classical error correction, showing exponential suppression of logical error and extending logical memory fivefold beyond the single-bit idling lifetime., Comment: BH and JS contributed equally to this work

Smart grid interoperability is the means to achieve the twin green and digital transition but re-mains heterogeneous and fragmented to date. This work presents the first ideas and corner-stones of an Interoperability Roadmap for the Energy Transition that is being developed by the Horizon Europe int:net project. This roadmap builds on four cornerstones that address open interoperability issues. These are a knowledge base to address the lack of convergence among existing initiatives, a maturity model and a network of testing and certification facilities to ad-dress the lack of practical tools for the industry, and a governance process to address the gap between standards-related approaches of Standards Development Organisations and Research and Innovation projects. A community of practice will be set up to ensure the continuity of the ongoing activities related to smart grid interoperability. To outlive the duration of the int:net project, the aim is to formalise the community of practice as a legal entity., Comment: 12. (Hybrid) Symposium Communications for Energy Systems (ComForEn 2023)

In order for optical cavities to enable strong light-matter interactions for quantum metrology, networking, and scalability in quantum computing systems, their mirrors must have minimal losses. However, high-finesse dielectric cavity mirrors can degrade in ultra-high vacuum (UHV), increasing the challenges of upgrading to cavity-coupled quantum systems. We observe the optical degradation of high-finesse dielectric optical cavity mirrors after high-temperature UHV bake in the form of a substantial increase in surface roughness. We provide an explanation of the degradation through atomic force microscopy (AFM), X-ray fluorescence (XRF), selective wet etching, and optical measurements. We find the degradation is explained by oxygen reduction in Ta$_2$O$_5$ followed by growth of tantalum sub-oxide defects with height to width aspect ratios near ten. We discuss the dependence of mirror loss on surface roughness and finally give recommendations to avoid degradation to allow for quick adoption of cavity-coupled systems.

Achieving the quantum noise targets of third-generation detectors will require 10 dB of squeezed-light enhancement as well as megawatt laser power in the interferometer arms - both of which require unprecedented control of the internal optical losses. In this work, we present a novel optimization approach to gravitational-wave detector design aimed at maximizing the robustness to common, yet unavoidable, optical fabrication and installation errors, which have caused significant loss in Advanced LIGO. As a proof of concept, we employ these techniques to perform a two-part optimization of the LIGO A+ design. First, we optimize the arm cavities for reduced scattering loss in the presence of point absorbers, as currently limit the operating power of Advanced LIGO. Then, we optimize the signal recycling cavity for maximum squeezing performance, accounting for realistic errors in the positions and radii of curvature of the optics. Our findings suggest that these techniques can be leveraged to achieve substantially greater quantum noise performance in current and future gravitational-wave detectors., Comment: Matches version accepted in PRD

Research in point cloud analysis with deep neural networks has made rapid progress in recent years. The pioneering work PointNet offered a direct analysis of point clouds. However, due to its architecture PointNet is not able to capture local structures. To overcome this drawback, the same authors have developed PointNet++ by applying PointNet to local areas. The local areas are defined by center points and their neighbors. In PointNet++ and its further developments the center points are determined with a Farthest Point Sampling (FPS) algorithm. This has the disadvantage that the center points in general do not have meaningful local areas. In this paper, we introduce the neural Local-Area-Learning Network (LocAL-Net) which places emphasis on the selection and characterization of the local areas. Our approach learns critical points that we use as center points. In order to strengthen the recognition of local structures, the points are given additional metric properties depending on the local areas. Finally, we derive and combine two global feature vectors, one from the whole point cloud and one from all local areas. Experiments on the datasets ModelNet10/40 and ShapeNet show that LocAL-Net is competitive for part segmentation. For classification LocAL-Net outperforms the state-of-the-arts.