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Abstract Alumina Josephson junction has demonstrated a tremendous potential to realize superconducting qubits. Further progress towards scalable superconducting qubits urgently needs to be guided by novel analysis mechanisms or methods to reduce the thickness sensitivity of the junction critical current to the tunnel barrier. Here, it is first revealed that the termination mode of AlOx interface plays a crucial role in the uniformity of critical current, and we demonstrate that the O-terminated interface has the lowest resistance sensitivity to thickness. More impressively, we developed atomically structured three-dimensional models and calculated their transport properties using a combination of quantum ballistic transport theory with first-principles DFT and NEGF to examine the effects of the Al2O3 termination mode and thickness variations. This work clarifies that O-terminated interface can effectively improve the resistance uniformity of Josephson junction, offering useful guidance for increasing the yield of fixed-frequency multi-qubit quantum chips which require tight control on qubit frequency.

Abstract Quantum computers have already shown significant potential to solve specific problems more efficiently than conventional supercomputers. A major challenge towards noisy intermediate-scale quantum computing is characterizing and reducing the various control costs. Quantum programming describes the process of quantum computation as a sequence, whose elements are selected from a finite set of universal quantum gates. Quantum compilation translates quantum programs to ordered pulses to the quantum control devices subsequently and quantum compilation optimization provides a high-level solution to reduce the control cost efficiently. Here, we propose a high-performance compilation strategy for multiplexing quantum control architecture. For representative benchmarks, the utilization efficiency of control devices increased by 49.44% on average in our work, with an acceptable circuit depth expansion executing on several real superconducting quantum computers of IBM.

Abstract In the field of network security, although there has been related work on software vulnerability detection based on classic machine learning, detection ability is directly proportional to the scale of training data. A quantum neural network has been proven to solve the memory bottleneck problem of classical machine learning, so it has far-reaching prospects in the field of vulnerability detection. To fill the gap in this field, we propose a quantum neural network structure named QDENN for software vulnerability detection. This work is the first attempt to implement word embedding of vulnerability codes based on a quantum neural network, which proves the feasibility of a quantum neural network in the field of vulnerability detection. Experiments demonstrate that our proposed QDENN can effectively solve the inconsistent input length problem of quantum neural networks and the problem of batch processing with long sentences. Furthermore, it can give full play to the advantages of quantum computing and realize a vulnerability detection model at the cost of a small amount of measurement. Compared to other quantum neural networks, our proposed QDENN can achieve higher vulnerability detection accuracy. On the sub dataset with a small-scale interval, the model accuracy rate reaches 99%. On each subinterval data, the best average vulnerability detection accuracy of the model reaches 86.3%.

With the wide application of drone technology, there is an increasing demand for the detection of radar return signals from drones. Existing detection methods mainly rely on time-frequency domain feature extraction and classical machine learning algorithms for image recognition. This method suffers from the problem of large dimensionality of image features, which leads to large input data size and noise affecting learning. Therefore, this paper proposes to extract signal time-domain statistical features for radar return signals from drones and reduce the feature dimension from 512 × 4 to 16 dimensions. However, the downscaled feature data makes the accuracy of traditional machine learning algorithms decrease, so we propose a new hybrid quantum neural network with signal feature overlay projection (HQNN-SFOP), which reduces the dimensionality of the signal by extracting the statistical features in the time domain of the signal, introduces the signal feature overlay projection to enhance the expression ability of quantum computation on the signal features, and introduces the quantum circuits to improve the neural network’s ability to obtain the inline relationship of features, thus improving the accuracy and migration generalization ability of drone detection. In order to validate the effectiveness of the proposed method, we experimented with the method using the MM model that combines the real parameters of five commercial drones and random drones parameters to generate data to simulate a realistic environment. The results show that the method based on statistical features in the time domain of the signal is able to extract features at smaller scales and obtain higher accuracy on a dataset with an SNR of 10 dB. On the time-domain feature data set, HQNN-SFOP obtains the highest accuracy compared to other conventional methods. In addition, HQNN-SFOP has good migration generalization ability on five commercial drones and random drones data at different SNR conditions. Our method verifies the feasibility and effectiveness of signal detection methods based on quantum computation and experimentally demonstrates that the advantages of quantum computation for information processing are still valid in the field of signal processing, it provides a highly efficient method for the drone detection using radar return signals. [ABSTRACT FROM AUTHOR]

Background: The development of the human vermiform appendix at the cellular level, as well as its function, is not well understood. Appendicitis in preschool children, although uncommon, is associated with a high perforation rate and increased morbidity. Methods: We performed single-cell RNA sequencing (scRNA-seq) on the human appendix during fetal and pediatric stages as well as preschool-age inflammatory appendices. Transcriptional features of each cell compartment were discussed in the developing appendix. Cellular interactions and differentiation trajectories were also investigated. We compared scRNA-seq profiles from preschool appendicitis to those of matched healthy controls to reveal disease-associated changes. Bulk transcriptomic data, immunohistochemistry, and real-time quantitative PCR were used to validate the findings. Results: Our analysis identified 76 cell types in total and described the cellular atlas of the developing appendix. We discovered the potential role of the BMP signaling pathway in appendiceal epithelium development and identified HOXC8 and PITX2 as the specific regulons of appendix goblet cells. Higher pericyte coverage, endothelial angiogenesis, and goblet mucus scores together with lower epithelial and endothelial tight junction scores were found in the preschool appendix, which possibly contribute to the clinical features of preschool appendicitis. Preschool appendicitis scRNA-seq profiles revealed that the interleukin-17 signaling pathway may participate in the inflammation process. Conclusions: Our study provides new insights into the development of the appendix and deepens the understanding of appendicitis in preschool children. [ABSTRACT FROM AUTHOR]

Accumulating animal studies have demonstrated associations between ambient air pollution (AP) and metabolic dysfunction-associated fatty liver disease (MAFLD), but relevant epidemiological evidence is limited. We evaluated the association of long-term exposure to AP with the risk of incident MAFLD in Northwest China. The average AP concentration between baseline and follow-up was used to assess individual exposure levels. Cox proportional hazard models and restricted cubic spline functions (RCS) were used to estimate the association of PM2.5 and its constituents with the risk of MAFLD and the dose–response relationship. Quantile g-computation was used to assess the joint effects of mixed exposure to air pollutants on MAFLD and the weights of the various pollutants. We observed 1516 cases of new-onset MAFLD, with an incidence of 10.89%. Increased exposure to pollutants was significantly associated with increased odds of MAFLD, with hazard ratios (HRs) of 2.93 (95% CI: 1.22, 7.00), 2.86 (1.44, 5.66), 7.55 (3.39, 16.84), 4.83 (1.89, 12.38), 3.35 (1.35, 8.34), 1.89 (1.02, 1.62) for each interquartile range increase in PM2.5, SO42−, NO3−, NH4+, OM, and BC, respectively. Stratified analyses suggested that females, frequent exercisers and never-drinkers were more susceptible to MAFLD associated with ambient PM2.5 and its constituents. Mixed exposure to SO42−, NO3−, NH4+, OM and BC was associated with an increased risk of MAFLD, and the weight of BC had the strongest effect on MAFLD. Exposure to ambient PM2.5 and its constituents increased the risk of MAFLD. [ABSTRACT FROM AUTHOR]

Abstract Background Prenatal synthetic glucocorticoid (sGC) exposure increases the susceptibility to cognitive and affective disorders in postnatal life. We previously demonstrated that prenatal sGC exposure results in an increase in corticotropin-releasing hormone (CRH) receptor type 1 (CRHR1) expression in the hippocampus of rats, and CRHR1 is involved in synapse formation via regulation of C-X-C chemokine ligand 5 (CXCL5) in hippocampus. We sought to investigate that the roles of CRHR1 and CXCL5 in learning and memory impairment caused by prenatal sGC exposure. Methods Pregnant rats were administered with saline or dexamethasone (DEX) from gestational day (GD) 14 to GD21. DEX offspring at 2-day old were treated with saline and CRHR1 antagonists (antalarmin and CP154526) for 7 days. Some DEX offspring received intra-hippocampal injection of AAV9 carrying CXCL5 gene. Spatial learning and memory was assessed by Morris water maze test. Immunofluorescence analysis was applied to show synapsin I and PSD95 signals in hippocampus. Synapsin I and PSD95 protein level and CXCL5 concentration were determined by western blotting and ELISA, respectively. Organotypic hippocampal slice cultures were used to investigate the effect of DEX on CXCL5 production in vitro. Results Both male and female DEX offspring displayed impairment of spatial learning and memory in adulthood. Synapsin I and PSD95 signals and CXCL5 levels were decreased in DEX offspring. DEX offspring with antalarmin and CP154526 treatment showed improved spatial learning and memory. Antalarmin and CP154526 treatment increased synapsin I and PSD95 signals and CXCL5 concentration in hippocampus. Bilaterally hippocampal injection of AAV9 carrying CXCL5 gene improved the spatial learning and memory and increased CXCL5 concentration and synapsin I and PSD95 levels in hippocampus. DEX dose-dependently suppressed CXCL5 production in cultured hippocammpal slices, which was prevented by antalarmin treatment. Conclusion CRHR1 and CXCL5 signaling in the hippocampus are involved in spatial learning and memory deficits caused by prenatal DEX exposure. CRHR1 activation contributes to decreased CXCL5 production in hippocampus induced by prenatal DEX treatment. Our study provides a molecular basis of prenatal GC exposure programming spatial learning and memory.