Search

Abstract The interfacial microstructures of Josephson junctions are vital for understanding the microscopic mechanism to improve the performance of superconducting qubits further. However, there remain significant concerns about well understanding the correlation between atomic structures and electrical behaviors. Here, we propose a new method to define the interface of the barrier in Josephson junctions, and investigate the factors that affect the electrical properties of junctions using material analysis techniques and first principles. We find that the aluminium–oxygen ratio of the interface contributes greatly to the electrical properties of junctions, which is consistent with the conclusions obtained by utilizing the generative adversarial network for data augmentation. When the aluminium–oxygen ratio of the interface is 0.67–1.1, the model exhibits a lower barrier height and better electrical properties of the junction. Moreover, when the thickness of the barrier is fixed, the impact of the aluminium–oxygen ratio becomes prominent. A detailed analysis of Josephson junctions using a microscopic model has led to identifying of process defects that can enhance the yield rate of chips. It has a great boost for determining the relationship between microstructures and macroscopic performances.

Abstract As a generalized quantum machine learning model, parameterized quantum circuits (PQC) have been found to perform poorly in terms of classification accuracy and model scalability for multi-category classification tasks. To address this issue, we propose a scalable parameterized quantum circuits classifier (SPQCC), which performs per-channel PQC and combines the measurements as the output of the trainable parameters of the classifier. By minimizing the cross-entropy loss through optimizing the trainable parameters of PQC, SPQCC leads to a fast convergence of the classifier. The parallel execution of identical PQCs on different quantum machines with the same structure and scale reduces the complexity of classifier design. Classification simulations performed on the MNIST Dataset show that the accuracy of our proposed classifier far exceeds that of other quantum classification algorithms, achieving the state-of-the-art simulation result and surpassing/reaching classical classifiers with a considerable number of trainable parameters. Our classifier demonstrates excellent scalability and classification performance.

Abstract In recent years, deep learning has been widely used in vulnerability detection with remarkable results. These studies often apply natural language processing (NLP) technologies due to the natural similarity between code and language. Since NLP usually consumes a lot of computing resources, its combination with quantum computing is becoming a valuable research direction. In this paper, we present a Recurrent Quantum Embedding Neural Network (RQENN) for vulnerability detection. It aims to reduce the memory consumption of classical models for vulnerability detection tasks and improve the performance of quantum natural language processing (QNLP) methods. We show that the performance of RQENN achieves the above goals. Compared with the classic model, the space complexity of each stage of its execution is exponentially reduced, and the number of parameters used and the number of bits consumed are significantly reduced. Compared with other QNLP methods, RQENN uses fewer qubit resources and achieves a 15.7% higher accuracy in vulnerability detection.

Abstract Quantum algorithms have shown their superiority in many application fields. However, a general quantum algorithm for numerical integration, an indispensable tool for processing sophisticated science and engineering issues, is still missing. Here, we first proposed a quantum integration algorithm suitable for any continuous functions that can be approximated by polynomials. More impressively, the algorithm achieves quantum encoding of any integrable functions through polynomial approximation, then constructs a quantum oracle to mark the number of points in the integration area and finally converts the statistical results into the phase angle in the amplitude of the superposition state. The quantum algorithm introduced in this work exhibits quadratic acceleration over the classical integration algorithms by reducing computational complexity from O(N) to O(√N). Our work addresses the crucial impediments for improving the generality of quantum integration algorithm, which provides a meaningful guidance for expanding the superiority of quantum computing.

[Objective] The purpose of this study was to analyze the influence of natural risks on the allocation efficiency of agricultural land resources and mechanism based on the difference of risk coping capability, and provide a theoretical basis and reference for improving the allocation efficiency of agricultural land resources. [Methods] Based on the survey data from Jiangsu, Jilin, and Sichuan Provinces, a multiple regression model was used to analyze the impact of natural risks on the efficiency of agricultural land resource allocation. The study also revealed the mechanism of impact of natural risks on the efficiency of agricultural land resource allocation from the perspective of different farmers’ risk coping capabilities. [Results] The findings show that: (1) There is a low allocation efficiency of farmland resources in China at this stage. Natural risks aggravate the mismatch of farmland resources, but there is a certain threshold of risk level. Only when the natural risk rises to a certain level, will it reduce the allocation efficiency of farmland resources. (2) High natural risks will inhibit the transfer of farmland to more efficient large-scale households, thereby hindering the improvement of farmland resource. The risk coping capability of large-scale farming households plays a positive moderating role in the effect of natural risks on the allocation efficiency of agricultural land resources, that is, higher risk coping capability of large-scale farming households will weaken the negative impact of natural risks on the allocation efficiency of agricultural land resources. [Conclusion] Enhancing the risk coping capability of agricultural operators is an important approach for improving the allocation efficiency of farmland resources. The government should focus on improving the natural risk management system, addressing endowment constraints and regulating the farmland transfer market to enhance the natural risk coping capability of agricultural operators, thereby effectively improving the allocation efficiency of agricultural land resources.

Pediatric rhabdomyosarcoma of the biliary tract (BRMS) is extremely rare. Here, we present a case of a 2-year-old child who was initially misdiagnosed with choledocholithiasis upon admission. The diagnosis was later confirmed as BRMS through endoscopic retrograde cholangiopancreatography (ERCP). The patient was cured through surgery followed by chemotherapy. Due to the lack of specific early symptoms and the challenges in imaging differentiation, particularly in pediatric patients, clinical awareness of this condition needs to be heightened. Our findings indicate that ERCP is currently the optimal diagnostic tool for this disease, and a combination of surgery and chemotherapy can yield better therapeutic outcomes.

In order to investigate the regulatory mechanism of exogeneous calcium chloride (CaCl2) on postharvest energy metabolism and ascertain the association between energy metabolism levels and color change in ‘Xiaobai’ apricots, ‘Xiaobai’ apricots from Xinjiang were fumigated with 3 g/100 mL CaCl2 solution or distilled water (control) before being stored at (10 ± 1.0) ℃ and relative humidity of (80 ± 5)%. Changes in chlorophyll, carotenoid, adenosine triphosphate (ATP), adenosine diphosphate (ADP), adenosine monophosphate (AMP) contents and energy charge (EC) levels in the peel and pulp of apricots and changes in enzyme activities and gene expression related to energy metabolism were determined during the storage period. The results showed that compared with the control group, CaCl2 treatment delayed chlorophyll degradation, inhibited carotenoid synthesis, maintained ATP and ADP contents and energy charge levels, enhanced the activities of H+-ATPase, Ca2+-ATPase, succinate dehydrogenase (SDH) and cytochrome c oxidase (CCO), and induced the expression of the PaH+-ATPase1, PaCa2+-ATPase10, PaSDH and PaCCO6 genes in both peel and pulp. Chlorophyll content in the peel and pulp was positively correlated with ATP, ADP and EC levels, but negatively correlated with carotenoid contents. There was a significantly positive correlation between chlorophyll and ADP levels in the peel and pulp (P < 0.01). It was suggested that exogenous CaCl2 could delay postharvest color change and maintain high energy charge by regulating energy metabolism, therefore prolonging the shelf life of ‘Xiaobai’ apricots.

Abstract In Noisy Intermediate-Scale Quantum (NISQ) era, the scarcity of qubit resources has prevented many quantum algorithms from being implemented on quantum devices. Circuit cutting technology has greatly alleviated this problem, which allows us to run larger quantum circuits on real quantum machines with currently limited qubit resources at the cost of additional classical overhead. However, the classical overhead of circuit cutting grows exponentially with the number of cuts and qubits, and the excessive postprocessing overhead makes it difficult to apply circuit cutting to large scale circuits. In this paper, we propose a fast reconstruction algorithm based on Hamiltonian Monte Carlo (HMC) sampling, which samples the high probability solutions by Hamiltonian dynamics from state space with dimension growing exponentially with qubit. Our algorithm avoids excessive computation when reconstructing the original circuit probability distribution, and greatly reduces the circuit cutting post-processing overhead. The improvement is crucial for expanding of circuit cutting to a larger scale on NISQ devices.

Abstract Binary code similarity analysis is widely used in the field of vulnerability search where source code may not be available to detect whether two binary functions are similar or not. Based on deep learning and natural processing techniques, several approaches have been proposed to perform cross-platform binary code similarity analysis using control flow graphs. However, existing schemes suffer from the shortcomings of large differences in instruction syntaxes across different target platforms, inability to align control flow graph nodes, and less introduction of high-level semantics of stability, which pose challenges for identifying similar computations between binary functions of different platforms generated from the same source code. We argue that extracting stable, platform-independent semantics can improve model accuracy, and a cross-platform binary function similarity comparison model N_Match is proposed. The model elevates different platform instructions to the same semantic space to shield their underlying platform instruction differences, uses graph embedding technology to learn the stability semantics of neighbors, extracts high-level knowledge of naming function to alleviate the differences brought about by cross-platform and cross-optimization levels, and combines the stable graph structure as well as the stable, platform-independent API knowledge of naming function to represent the final semantics of functions. The experimental results show that the model accuracy of N_Match outperforms the baseline model in terms of cross-platform, cross-optimization level, and industrial scenarios. In the vulnerability search experiment, N_Match significantly improves hit@N, the mAP exceeds the current graph embedding model by 66%. In addition, we also give several interesting observations from the experiments. The code and model are publicly available at https://www.github.com/CSecurityZhongYuan/Binary-Name_Match .