Two-qubit quantum codes have been suggested to obtain better efficiency and higher loss tolerance in quantum key distribution. Here, we propose a two-qubit quantum key distribution protocol based on a mixed basis consisting of two Bell states and two states from the computational basis. All states can be generated from a single entangled photon pair resource by using local operations on only one auxiliary photon. Compared to other schemes it is also possible to deterministically discriminate all states using linear optics. Additionally, our protocol can be implemented with today's technology. When discussing the security of our protocol we find a much improved resistance against certain attacks as compared to the standard BB84 protocol.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1364/OE.25.023545 | DOI Listing |
Sci Rep
January 2025
College of Information Science and Technology, Hainan Normal University, Haikou, 571158, China.
Breast cancer is one of the most aggressive types of cancer, and its early diagnosis is crucial for reducing mortality rates and ensuring timely treatment. Computer-aided diagnosis systems provide automated mammography image processing, interpretation, and grading. However, since the currently existing methods suffer from such issues as overfitting, lack of adaptability, and dependence on massive annotated datasets, the present work introduces a hybrid approach to enhance breast cancer classification accuracy.
View Article and Find Full Text PDFLight Sci Appl
January 2025
National Research Center for High-Efficiency Grinding, College of Mechanical and Vehicle Engineering, Hunan University, 410082, Changsha, China.
Accurately and swiftly characterizing the state of polarization (SoP) of complex structured light is crucial in the realms of classical and quantum optics. Conventional strategies for detecting SoP, which typically involves a sequence of cascaded optical elements, are bulky, complex, and run counter to miniaturization and integration. While metasurface-enabled polarimetry has emerged to overcome these limitations, its functionality predominantly remains confined to identifying SoP within the standard Poincaré sphere framework.
View Article and Find Full Text PDFSci Bull (Beijing)
January 2025
Jiangsu Key Laboratory for Carbon-Based Functional Materials & Devices, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou 215123, China; Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Zhuhai MUST Science and Technology Research Institute, Macau University of Science and Technology, Macao 999078, China; Institute of Organic Optoelectronics (IOO), Jiangsu Industrial Technology Research Institute (JITRI), Suzhou 215200, China. Electronic address:
High-quality quantum dots (QDs) possess superior electroluminescent efficiencies and ultra-narrow emission linewidths are essential for realizing ultra-high definition QD light-emitting diodes (QLEDs). However, the synthesis of such QDs remains challenging. In this study, we present a facile high-temperature successive ion layer adsorption and reaction (HT-SILAR) strategy for the growth of precisely tailored ZnCdSe/ZnSe shells, and the consequent production of high-quality, large-particle, alloyed red CdZnSe/ZnCdSe/ZnSe/ZnS/CdZnS QDs.
View Article and Find Full Text PDFSci Bull (Beijing)
January 2025
Key Laboratory of Ocean Observation-Imaging Testbed of Zhejiang Province, Ocean College, Zhejiang University, Hangzhou 310058, China. Electronic address:
Mol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!