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Cyanobacteria can form complex interactions with heterotrophic microorganisms, but this relationship is susceptible to nutrient concentrations. Disentangling the cyanobacteria–bacteria interactions in relation to nutrient supply is essential to understanding their roles in geochemical cycles under global change. We hypothesize that enhanced nutrient supply in oligotrophic oceans can promote interactions among cyanobacteria and bacteria. Therefore, we investigated the planktonic bacteria and their interactions with cyanobacteria in relation to elevated nutrients caused by enhanced upwelling around a shallow and a deep seamount in the tropical western Pacific Ocean. We found obviously higher complexity of network occurred with significantly more cyanobacteria in the deep chlorophyll maximum layer of the shallow seamount when compared with that of the deep seamount. Cyanobacteria can shape bacterial interaction and community evenness in response to relatively high nutrient concentrations. The effects of the nutrients on cyanobacteria-related networks were further estimated based on the Tara Oceans data. Statistical analyses further showed a facilitative effect of nitrate concentrations on cyanobacteria–bacteria mutualistic interactions in the global oligotrophic ocean. By analysing the Tara Ocean macrogenomic data, we detected functional genes related to cyanobacteria–bacteria interactions in all samples, indicating the existence of a mutualistic relationship. Our results reveal cyanobacteria–bacteria interaction in response to nutrient elevation in oligotrophic ocean and highlight the potentially negative effects of global change on the bacterial community from the view of the bio-interaction. [ABSTRACT FROM AUTHOR]

Prior studies have demonstrated that deep learning-based approaches can enhance the performance of source code vulnerability detection by training neural networks to learn vulnerability patterns in code representations. However, due to limitations in code representation and neural network design, the validity and practicality of the model still need to be improved. Additionally, due to differences in programming languages, most methods lack cross-language detection generality. To address these issues, in this paper, we analyze the shortcomings of previous code representations and neural networks. We propose a novel hierarchical code representation that combines Concrete Syntax Trees (CST) with Program Dependence Graphs (PDG). Furthermore, we introduce a Tree-Graph-Gated- Attention (TGGA) network based on gated recurrent units and attention mechanisms to build a Hierarchical Code Representation learning-based Vulnerability Detection (HCRVD) system. This system enables cross-language vulnerability detection at the function-level. The experiments show that HCRVD surpasses many competitors in vulnerability detection capabilities. It benefits from the hierarchical code representation learning method, and outperforms baseline in cross-language vulnerability detection by 9.772% and 11.819% in the C/C++ and Java datasets, respectively. Moreover, HCRVD has certain ability to detect vulnerabilities in unknown programming languages and is useful in real open-source projects. HCRVD shows good validity, generality and practicality. [ABSTRACT FROM AUTHOR]

Although the performance of qubits has been improved in recent years, the differences in the microscopic atomic structure of the Josephson junctions, the core devices prepared under different preparation conditions, are still underexplored. In this paper, the effects of the oxygen temperature and upper aluminum deposition rate on the topology of the barrier layer in the aluminum-based Josephson junctions have been presented by classical molecular dynamics simulations. We apply a Voronoi tessellation method to characterize the topology of the interface and central regions of the barrier layers. We find that when the oxygen temperature is 573 K and the upper aluminum deposition rate is 4 Å/ps, the barrier has the fewest atomic voids and the most closely arranged atoms. However, if only the atomic arrangement of the central region is considered, the optimal rate of the aluminum deposition is 8 Å/ps. This work provides microscopic guidance for the experimental preparation of Josephson junctions, which helps to improve the performance of qubits and accelerate the practical application of quantum computers.

All-nitride Josephson junctions are being actively explored for applications in superconducting quantum chips because of their unique advantages including their antioxidant chemical stability and high crystal quality. However, the theoretical research on their microstructure mechanism that determines transport properties is still absent, especially on the defects. In this paper, we apply the first principles and non-equilibrium Green’s function to calculate the electrical transport characteristics of the yellow preset model. It is first revealed that the N-vacancy defects play a crucial role in determining the conductivity of the NbN-based Josephson junctions, and demonstrate the importance for the uniformity of vacancy distribution. It is found that the uniform number of vacancies can effectively increase the conductance of Josephson junction, but the position distribution of vacancies has little effect on the conductance. The work clarifies the effect of the N-vacancy defects on the conductivity of the NbN-based Josephson junctions, which offers useful guidance for understanding the microscope mechanism of the NbN-based Josephson junction, thus showing a great prospect in the improvement of the yield of superconducting quantum chips in the future.

The K-nearest neighbor (KNN) algorithm is one of the most extensively used classification algorithms, while its high time complexity limits its performance in the era of big data. The quantum K-nearest neighbor (QKNN) algorithm can handle the above problem with satisfactory efficiency; however, its accuracy is sacrificed when directly applying the traditional similarity measure based on Euclidean distance. Inspired by the Polar coordinate system and the quantum property, this work proposes a new similarity measure to replace the Euclidean distance, which is defined as Polar distance. Polar distance considers both angular and module length information, introducing a weight parameter adjusted to the specific application data. To validate the efficiency of Polar distance, we conducted various experiments using several typical datasets. For the conventional KNN algorithm, the accuracy performance is comparable when using Polar distance for similarity measurement, while for the QKNN algorithm, it significantly outperforms the Euclidean distance in terms of classification accuracy. Furthermore, the Polar distance shows scalability and robustness superior to the Euclidean distance, providing an opportunity for the large-scale application of QKNN in practice.

Image matching is an important research topic in computer vision and image processing. However, existing quantum algorithms mainly focus on accurate matching between template pixels, and are not robust to changes in image location and scale. In addition, the similarity calculation of the matching process is a fundamentally important issue. Therefore, this paper proposes a hybrid quantum algorithm, which uses the robustness of SIFT (scale-invariant feature transform) to extract image features, and combines the advantages of quantum exponential storage and parallel computing to represent data and calculate feature similarity. Finally, the quantum amplitude estimation is used to extract the measurement results and realize the quadratic acceleration of calculation. The experimental results show that the matching effect of this algorithm is better than the existing classical architecture. Our hybrid algorithm broadens the application scope and field of quantum computing in image processing.

The quantum computer has been claimed to show more quantum advantage than the classical computer in solving some specific problems. Many companies and research institutes try to develop quantum computers with different physical implementations. Currently, most people only focus on the number of qubits in a quantum computer and consider it as a standard to evaluate the performance of the quantum computer intuitively. However, it is quite misleading in most times, especially for investors or governments. This is because the quantum computer works in a quite different way than classical computers. Thus, quantum benchmarking is of great importance. Currently, many quantum benchmarks are proposed from different aspects. In this paper, we review the existing performance benchmarking protocols, models, and metrics. We classify the benchmarking techniques into three categories: physical benchmarking, aggregative benchmarking, and application-level benchmarking. We also discuss the future trend for quantum computer’s benchmarking and propose setting up the QTOP100.

To investigate whether the addition of milk fat globule membrane (MFGM) materials could improve the fat stability of infant formula and study the mechanism of membrane construction of fat globules, MFGM materials were added to infant formula in raw materials. The results showed that the optimal additional amount of MFGM materials was 0.5% to achieve better fat stability in the infant formula. The concentration of interfacial proteins in the reconstituted milk‐added MFGM materials was higher than in the control sample. Moreover, the laser confocal scanning microscopy image showed that MFGM materials were involved in the membrane construction of fat globules. [ABSTRACT FROM AUTHOR]

As cloud quantum computing gains broader acceptance, a growing quantity of researchers are directing their focus towards this domain. Nevertheless, the rapid surge in demand for cloud-based quantum computing resources has led to a scarcity, which in turn hampers users from achieving optimal satisfaction. Therefore, cloud quantum computing service providers require a unified analysis and scheduling framework for their quantumresources and user jobs to meet the ever-growing usage demands. This paper introduces a new multi-programming scheduling framework for quantum computing in a cloud environment. The framework addresses the issue of limited quantum computing resources in cloud environments and ensures a satisfactory user experience. It introduces three innovative designs: 1) Our framework automatically allocates tasks to different quantum backends while ensuring fairness among users by considering both the cloud-based quantum resources and the user-submitted tasks. 2)Multi-programmingmechanism is employed across different quantum backends to enhance the overall throughput of the quantum cloud. In comparison to conventional task schedulers, our proposed framework achieves a throughput improvement of more than two-fold in the quantum cloud. 3) The framework can balance fidelity and user waiting time by adaptively adjusting scheduling parameters. [ABSTRACT FROM AUTHOR]