Long-distance quantum communication and networking require quantum memory nodes with efficient optical interfaces and long memory times. We report the realization of an integrated two-qubit network node based on silicon-vacancy centers (SiVs) in diamond nanophotonic cavities. Our qubit register consists of the SiV electron spin acting as a communication qubit and the strongly coupled silicon-29 nuclear spin acting as a memory qubit with a quantum memory time exceeding 2 seconds. By using a highly strained SiV, we realize electron-photon entangling gates at temperatures up to 1.5 kelvin and nucleus-photon entangling gates up to 4.3 kelvin. We also demonstrate efficient error detection in nuclear spin-photon gates by using the electron spin as a flag qubit, making this platform a promising candidate for scalable quantum repeaters.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1126/science.add9771 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Optimizing papermaking wastewater treatment by predicting effluent quality with node-level capsule graph neural networks.
Environ Monit Assess
January 2025
Civil Engineering, SRM Institute of Science and Technology, Kattankulathur, 603203, India.
Papermaking wastewater consists of a sizable amount of industrial wastewater; hence, real-time access to precise and trustworthy effluent indices is crucial. Because wastewater treatment processes are complicated, nonlinear, and time-varying, it is essential to adequately monitor critical quality indices, especially chemical oxygen demand (COD). Traditional models for predicting COD often struggle with sensitivity to parameter tuning and lack interpretability, underscoring the need for improvement in industrial wastewater treatment.
View Article and Find Full Text PDFNon-alcoholic fatty liver disease is associated with structural covariance network reconfiguration in cognitively unimpaired adults with type 2 diabetes.
Neuroscience
January 2025
Department of Radiology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008 China; Medical Imaging Center, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008 China; Institute of Medical Imaging and Artificial Intelligence, Nanjing University, Nanjing 210008 China; Institute of Brain Science, Nanjing University, Nanjing, China. Electronic address:
Type 2 diabetes (T2D) is often accompanied by non-alcoholic fatty liver disease (NAFLD), both of which are related to brain damage and cognitive impairment. However, cortical structural alteration and its relationship with metabolism and cognition in T2D with NAFLD (T2NAFLD) and without NAFLD (T2noNAFLD) remain unclear. The brain MRI scans, clinical measures and neuropsychological test were evaluated in 50 normal controls (NC), 73 T2noNAFLD, and 58 T2NAFLD.
View Article and Find Full Text PDFRetiform hemangioendothelioma: A case report and literature review.
Int J Surg Case Rep
January 2025
Department of Pathology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province 250025, China. Electronic address:
Introduction And Importance: Retiform hemangioendothelioma(RH) is a rare vascular tumor affecting patients over a wide age range without a gender predilection; only about 50 cases have been described so far.
Case Presentation: We report a case of submandibular retiform hemangioendothelioma in a 58-year-old woman who had been diagnosed with RH 20 years ago and had experienced recurrence four times during the past 20 years. This will increase the limited number of such cases in the hope of gaining a better understanding of this rare type of tumor.
Disentangled Active Learning on Graphs.
Neural Netw
January 2025
Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai 201804, China; National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security, Tongji University, Shanghai 201804, China.
Active learning on graphs (ALG) has emerged as a compelling research field due to its capacity to address the challenge of label scarcity. Existing ALG methods incorporate diversity into their query strategies to maximize the gains from node sampling, improving robustness and reducing redundancy in graph learning. However, they often overlook the complex entanglement of latent factors inherent in graph-structured data.
View Article and Find Full Text PDFGraph Neural Networks-Based Prediction of Drug Gene Interactions of RTK-VEGF4 Receptor Family in Periodontal Regeneration.
J Clin Exp Dent
December 2024
DDS. Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, Colombia.
Background: The RTK-VEGF4 receptor family, which includes VEGFR-1, VEGFR-2, and VEGFR-3, plays a crucial role in tissue regeneration by promoting angiogenesis, the formation of new blood vessels, and recruiting stem cells and immune cells. Machine learning, particularly graph neural networks (GNNs), has shown high accuracy in predicting these interactions. This study aims to predict drug-gene interactions of the RTK-VEGF4 receptor family in periodontal regeneration using graph neural networks.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!