资源环境科技发展态势分析平台

Screening mammography aims to identify breast cancer at earlier stages of the disease, when treatment can be more successful(1). Despite the existence of screening programmes worldwide, the interpretation of mammograms is affected by high rates of false positives and false negatives(2). Here we present an artificial intelligence (AI) system that is capable of surpassing human experts in breast cancer prediction. To assess its performance in the clinical setting, we curated a large representative dataset from the UK and a large enriched dataset from the USA. We show an absolute reduction of 5.7% and 1.2% (USA and UK) in false positives and 9.4% and 2.7% in false negatives. We provide evidence of the ability of the system to generalize from the UK to the USA. In an independent study of six radiologists, the AI system outperformed all of the human readers: the area under the receiver operating characteristic curve (AUC-ROC) for the AI system was greater than the AUC-ROC for the average radiologist by an absolute margin of 11.5%. We ran a simulation in which the AI system participated in the double-reading process that is used in the UK, and found that the AI system maintained non-inferior performance and reduced the workload of the second reader by 88%. This robust assessment of the AI system paves the way for clinical trials to improve the accuracy and efficiency of breast cancer screening.

Distributing entanglement over long distances using optical networks is an intriguing macroscopic quantum phenomenon with applications in quantum systems for advanced computing and secure communication(1,2). Building quantum networks requires scalable quantum light-matter interfaces(1) based on atoms(3), ions(4) or other optically addressable qubits. Solid-state emitters(5), such as quantum dots and defects in diamond or silicon carbide(6-10), have emerged as promising candidates for such interfaces. So far, it has not been possible to scale up these systems, motivating the development of alternative platforms. A central challenge is identifying emitters that exhibit coherent optical and spin transitions while coupled to photonic cavities that enhance the light-matter interaction and channel emission into optical fibres. Rare-earth ions in crystals are known to have highly coherent 4f-4f optical and spin transitions suited to quantum storage and transduction(11-15), but only recently have single rare-earth ions been isolated(16,17) and coupled to nanocavities(18,19). The crucial next steps towards using single rare-earth ions for quantum networks are realizing long spin coherence and single-shot readout in photonic resonators. Here we demonstrate spin initialization, coherent optical and spin manipulation, and high-fidelity single-shot optical readout of the hyperfine spin state of single Yb-171(3+) ions coupled to a nanophotonic cavity fabricated in an yttrium orthovanadate host crystal. These ions have optical and spin transitions that are first-order insensitive to magnetic field fluctuations, enabling optical linewidths of less than one megahertz and spin coherence times exceeding thirty milliseconds for cavity-coupled ions, even at temperatures greater than one kelvin. The cavity-enhanced optical emission rate facilitates efficient spin initialization and single-shot readout with conditional fidelity greater than 95 per cent. These results showcase a solid-state platform based on single coherent rare-earth ions for the future quantum internet.

Single ytterbium ion qubits in nanophotonic cavities have long coherence times and can be optically read out in a single shot, establishing them as excellent candidates for optical quantum networks.