http://hdl.handle.net/10453/35199 2026-07-17T00:09:00Z http://hdl.handle.net/10453/195688 Title: Dynamic Continuous Variable Quantum Key Distribution for Securing a Future Global Quantum Network Authors: Sayat, MT; Kish, SP; Lam, PK; Rattenbury, NJ; Cater, JE Abstract: Abstract Continuous variable quantum key distribution (CVQKD) is a developing method to secure information exchange in future quantum networks. With the recent developments in quantum technology and greater access to space, a global quantum network secured by CVQKD can be within reach. In this work, the structures of existing QKD networks are analyzed, and how they can be fit into a general overarching three‐layer QKD network architecture for the endeavor of a global QKD network. Such a network can comprise different links in fiber and free‐space. The finite size limit secret key rates (SKRs) with multidimensional reconciliation are calculated for the different links for which CVQKD can be used in such a network. The results show that CVQKD generally achieves longer distances and larger SKRs in inter‐satellite, satellite‐to‐ground, fiber, and underwater links in descending order. The different links and nodes are classified and secret key distribution is studied as a graph problem. The link capacity, a routing metric for secret key distribution, which considers a dynamic SKR based on dynamic links is presented. Its use in simulated CVQKD networks is presented for the aim of spatiotemporal secret key distribution through a dynamic CVQKD network. 2025-10-01T00:00:00Z http://hdl.handle.net/10453/195684 Title: Knowledge-aware contrastive heterogeneous molecular graph learning. Authors: Chen, M; Wu, J; Pan, S; Lin, F; Du, B; Gong, X; Hu, W Editors: Fariselli, P Abstract: Molecular representation learning is pivotal in predicting molecular properties and advancing drug design. Traditional methodologies, which predominantly rely on homogeneous graph encoding, are limited by their inability to integrate external knowledge and represent molecular structures across different levels of granularity. To address these limitations, we propose a paradigm shift by encoding molecular graphs into heterogeneous structures, introducing a novel framework: Knowledge-aware Contrastive Heterogeneous Molecular Graph Learning. This approach leverages contrastive learning to enrich molecular representations with embedded external knowledge. KCHML conceptualizes molecules through three distinct graph views-molecular, elemental, and pharmacological-enhanced by heterogeneous molecular graphs and a dual message-passing mechanism. This design offers a comprehensive representation for property prediction, as well as for downstream tasks such as drug-drug interaction prediction. Extensive benchmarking demonstrates KCHML's superiority over state-of-the-art molecular property prediction models, underscoring its ability to capture intricate molecular features. 2025-05-01T00:00:00Z http://hdl.handle.net/10453/195681 Title: Short-time simulation of quantum dynamics by Pauli measurements Authors: Faehrmann, PK; Eisert, J; Kieferová, M; Kueng, R Abstract: Simulating the dynamics of complex quantum systems is a central application of quantum devices. Here we propose leveraging the power of measurements to simulate short-time quantum dynamics of physically prepared quantum states in classical postprocessing using a truncated Taylor series approach. While limited to short simulation times, our hybrid quantum-classical method is equipped with rigorous error bounds. It is extendable to estimate low-order Taylor approximations of smooth time-dependent functions of tractable linear combinations of measurable operators. These insights can be made use of in the context of Hamiltonian learning and device verification, short-time imaginary-time evolution, or the application of intractable operations to subuniversal quantum simulators in classical postprocessing. 2025-07-01T00:00:00Z http://hdl.handle.net/10453/195675 Title: Isolation of two novel anti-glycation peptides from highland barley (Hordeum vulgare L.) protein hydrolysates: Structural characteristics and mechanism of action against advanced glycation end-products (AGEs) Authors: Phyo, SH; Siddique, MS; Khan, I; Li, C; Zhao, W 2025-09-01T00:00:00Z