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Entanglement has evolved from an enigmatic concept of quantum physics to a key ingredient of quantum technology. It explains correlations between measurement outcomes that contradict classical physics and has been widely explored with small sets of individual qubits. Multi-partite entangled states build up in gate-based quantum-computing protocols and-from a broader perspective-were proposed as the main resource for measurement-based quantum-information processing. The latter requires the ex-ante generation of a multi-qubit entangled state described by a graph. Small graph states such as Bell or linear cluster states have been produced with photons, but the proposed quantum-computing and quantum-networking applications require fusion of such states into larger and more powerful states in a programmable fashion. Here we achieve this goal by using an optical resonator containing two individually addressable atoms. Ring and tree graph states with up to eight qubits, with the names reflecting the entanglement topology, are efficiently fused from the photonic states emitted by the individual atoms. The fusion process itself uses a cavity-assisted gate between the two atoms. Our technique is, in principle, scalable to even larger numbers of qubits and is the decisive step towards, for instance, a memory-less quantum repeater in a future quantum internet.
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http://dx.doi.org/10.1038/s41586-024-07357-5 | DOI Listing |
J Chem Theory Comput
September 2025
Department of Materials Science and Engineering, City University of Hong Kong, Kowloon 999077, Hong Kong China.
Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as CG force fields, which have demonstrated success in CG simulations of proteins.
View Article and Find Full Text PDFJ Chem Phys
September 2025
Yusuf Hamied Department of Chemistry. Lensfield Road, Cambridge CB2 1EW, United Kingdom.
Folding and unfolding in molecules as simple as short hydrocarbons and as complicated as large proteins continue to be an active research field. Here, we investigate folding in n-C14H30 using both density functional theory (DFT)/B3LYP calculations of 27 772 local minima and a kinetic transition network calculated for a previously reported potential energy surface (PES) obtained by fitting roughly 250 000 B3LYP energies. In addition to generating a database of minima and the transition states that connect them, these calculations and the PES based on them have been used to develop a simple and accurate model for the energy landscape.
View Article and Find Full Text PDFFront Hum Neurosci
August 2025
Baptist Medical Center, Department of Behavioral Health, Jacksonville, FL, United States.
Introduction: This study investigates four subdomains of executive functioning-initiation, cognitive inhibition, mental shifting, and working memory-using task-based functional magnetic resonance imaging (fMRI) data and graph analysis.
Methods: We used healthy adults' functional magnetic resonance imaging (fMRI) data to construct brain connectomes and network graphs for each task and analyzed global and node-level graph metrics.
Results: The bilateral precuneus and right medial prefrontal cortex emerged as pivotal hubs and influencers, emphasizing their crucial regulatory role in all four subdomains of executive function.
Proc Mach Learn Res
November 2024
Pretraining plays a pivotal role in acquiring generalized knowledge from large-scale data, achieving remarkable successes as evidenced by large models in CV and NLP. However, progress in the graph domain remains limited due to fundamental challenges represented by feature heterogeneity and structural heterogeneity. Recent efforts have been made to address feature heterogeneity via Large Language Models (LLMs) on text-attributed graphs (TAGs) by generating fixed-length text representations as node features.
View Article and Find Full Text PDFChem Sci
August 2025
Engineering Research Center of Cell & Therapeutic Antibody (MOE), School of Pharmacy, Shanghai Jiao Tong University Shanghai 200240 China
Predicting Antibody-Antigen (Ab-Ag) docking and structure-based design represent significant long-term and therapeutically important challenges in computational biology. We present SAGERank, a general, configurable deep learning framework for antibody design using Graph Sample and Aggregate Networks. SAGERank successfully predicted the majority of epitopes in a cancer target dataset.
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