A general quantum algorithm for numerical integration.

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.

Fast reconstruction algorithm based on HMC sampling.

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.

Binary code similarity analysis based on naming function and common vector space.

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 .

Machine Learning Prediction Model and Risk Factor Analysis of Reoperation in Recurrent Lumbar Disc Herniation Patients After Percutaneous Endoscopic Lumbar Discectomy.

OBJECTIVE: To investigate the risk factors of reoperation after percutaneous endoscopic lumbar discectomy (PELD) due to recurrent lumbar disc herniation (rLDH) and to establish a set of individualized prediction models. METHODS: Patients who underwent PELD successfully from January 2016 to February 2022 in a single institution were enrolled in this study. Six methods of machine learning (ML) were used to establish an individualized prediction model for reoperation in rLDH patients after PELD, and these models were compared with logistics regression model to select optimal model. RESULTS: A total of 2603 patients were enrolled in this study. 57 patients had repeated operation due to rLDH and 114 patients were selected from the remaining 2546 nonrecurrent patients as matched controls. Multivariate logistic regression analysis showed that disc herniation type (P < .001), Modic changes (type II) (P = .003), sagittal range of motion (sROM) (P = .022), facet orientation (FO) (P = .028) and fat infiltration (FI) (P = .001) were independent risk factors for reoperation in rLDH patients after PELD. The XGBoost AUC was of 90.71%, accuracy was approximately 88.87%, sensitivity was 70.81%, specificity was 97.19%. The traditional logistic regression AUC was 77.4%, accuracy was about 77.73%, sensitivity was 47.15%, specificity was 92.12%. CONCLUSION: This study showed that disc herniation type (extrusion, sequestration), Modic changes (type II), a large sROM, a large FO and high FI were independent risk factors for reoperation in LDH patients after PELD. The prediction efficiency of XGBoost model was higher than traditional Logistic regression analysis model.

Effect of the Al/AlOx interfacial stacking sequence on the transport properties of alumina tunnel junctions.

Superconducting quantum bits based on Al/AlOx/Al Josephson junction devices are among the most developed quantum bits at present. The microstructure of the device interface critically affects the electrical properties of Josephson junctions, which in turn severely affects the superconducting quantum bits. Further progress towards scalable superconducting qubits urgently needs to be guided by novel analysis mechanisms or methods to improve the performance of junctions. A direct experimental study of the atomic structure of the device is very challenging. Therefore, we simulated three-dimensional Al/α-Al2O3/Al Josephson junction devices via first-principles electronic structure and ballistic transport calculations to investigate the relationship between transport properties and the Al/Al2O3 stacking sequence. This work elucidates in detail the effects of the aluminum and alumina stacking sequence on the electron transport properties of the Al/Al2O3/Al system at the microscopic level by combining first-principles density functional theory and a non-equilibrium Green's function formalism. It is first revealed that the oxygen termination mode exhibits the least sensitivity to conductance changes in the Al/Al2O3 stacking sequence, offering useful theoretical guidance for increasing the yield of fixed-frequency multi-qubit quantum chips which require tight control on qubit frequency.

Directly Controlling the Transport Properties of All-Nitride Josephson Junctions by N-Vacancy Defects.

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.

Computational Simulations of Fabrication of Aluminum-Based Josephson Junctions: Topological Aspects of the Barrier Structure.

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.

An Enhanced Quantum K-Nearest Neighbor Classification Algorithm Based on Polar Distance.

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.

Interpretation and reunderstanding of the updated pathological classification of renal tumors of World Health Organization in 2022 / 肿瘤研究与临床

In 2022, World Health Organization (WHO) launched the 5th edition of the classification of renal tumors. This classification continues to use the previous classification framework of renal tumors based on morphology and tissue structure, and proposes the concept of renal cell carcinoma defined by molecular features for the first time. This article interprets from the 3 aspects of historical changes of WHO classification and grading of renal tumors, comparison of 2022 and 2016 WHO classification of renal tumors, and the role of molecular characteristics in the new pathological types such as ELOC mutant renal cell carcinoma, ALK rearrangement renal cell carcinoma, eosinophilic solid and cystic renal cell carcinoma. The purpose is to better understand the WHO from the traditional classification system based on tissue morphology to a three-in-one integrated classification system covering morphology, immunophenotype and genetic characteristics, and to understand the important value of molecular pathology in guiding the work of pathologists and clinicians under the new classification system.

Succinate dehydrogenase deficiency renal cell carcinoma: a case report / 中华泌尿外科杂志

Succinate dehydrogenase (SDH) defective renal cell carcinoma (RCC) is a new subtype of renal carcinoma newly identified by WHO(2016). Until now, only a few samples and a few cases have been reported retrospectively. This article reported a young female patient who was found to have a small tumor in the left kidney by physical examination and underwent left partial nephrectomy. The postoperative pathological result was SDH-RCC. There was no recurrence and metastasis of the tumor 3 months after operation.

Clinical Efficacy of Niaoxue No.1 Prescription in Treatment of Henoch-Schönlein Purpura Nephritis with Blood Heat and Stasis Syndrome in Children / 中国实验方剂学杂志

ObjectiveTo investigate the clinical efficacy of Niaoxue No.1 Prescription in treating Henoch-Schönlein purpura （HSP） nephritis with blood heat and stasis syndrome and its effect on urine erythrocyte， urine protein， blood neutrophils， and blood routine-derived indicators. MethodA multicenter， randomized controlled trial （RCT） was conducted involving 108 HSP nephritis patients from three hospitals. The patients were randomly divided into a control group （54 cases） and a treatment group （54 cases）. The treatment group received Niaoxue No.1 prescription once daily， while the control group was treated with captopril and ferulic acid tablets. Both groups underwent a 4-week course of treatment. The urine erythrocyte， urine microalbumin （mAlb）， urine sediment red blood cell count， traditional Chinese medicine （TCM） syndrome score， 24-hour urine protein， blood neutrophil count， neutrophil to lymphocyte ratio （NLR）， platelet to lymphocyte ratio （PLR）， lymphocyte to monocyte ratio （LMR）， D-dimer， and immunoglobulin A were detected. The recurrence rate of HSP nephritis was followed up for 6 months. ResultThe total effective rates were 88.9% （48/54） in the treatment group and 70.4% （38/54） in the control group， and the treatment group was superior to the control group （χ2=5.708， P<0.05）. Compared with the results before treatment， after 14 days of treatment， the TCM syndrome total score， urine erythrocyte， urine mAlb， and 24-hour urine protein in both groups significantly decreased （P<0.05，P<0.01）， and the improvement was more significant in the treatment group than the control group （P<0.05）. After 28 days of treatment， compared with the results before treatment， the TCM syndrome total score， urine erythrocyte， urine mAlb， urine sediment red blood cell count， D-dimer， and 24-hour urine protein in both groups significantly decreased （P<0.05，P<0.01）， with the treatment group showing a more significant reduction in urine mAlb than the control group （P<0.05）. On the 14th and 28th days of treatment， the neutrophil percentage and NLR were lower in the treatment group than in the control group （P<0.05）， while there was no statistically significant difference in PLR and LMR. The recurrence rate of nephritis in both groups showed no statistically significant difference after a 6-month follow-up. ConclusionNiaoxue No.1 Prescription in the treatment of HSP nephritis with blood heat and stasis syndrome can significantly improve clinical symptoms， shorten the course of the disease， and reduce urine erythrocyte， urine mAlb， 24-hour urine protein， blood neutrophils， and NLR， thereby effectively alleviating the inflammatory state and reducing kidney damage in children with HSP nephritis.

Association between perfluorinated alkyl substances and depression / 环境与职业医学

Background Perfluorinated alkyl substances (PFAS) are a class of synthetic organic fluorides, which have adverse health effects on brain function, and limited research has been conducted on their effects on depression. Objective To assess potential correlation between serum PFAS and depression. Methods Using the 2015—2016 and 2017—2018 National Health and Nutrition Examination Survey (NHANES) datasets, 2626 subjects with complete relevant information in people ≥20 years old were selected. Logistic regression and restricted cubic splines were used to analyze the association and dose-response relationship between serum PFAS concentration and depression. Subgroup analysis was performed on sex, age, race, education level, marital status, family income to poverty ratio, moderate exercise, body mass index, and drinking status. Results Among the 2626 subjects, there were 666 patients (25.4%) with mild or above depression. After adjusting for race, education level, marital status, body mass index, moderate exercise, drinking history, cotinine, and other types of PFAS, serum perfluorooctane sulfonate (PFOS) was positively associated with the risk of depression (OR=1.85, 95%CI: 1.14, 3.02), and showed a nonlinear dose-response relationship (χ2=6.37, Pnonlinear=0.012). Perfluorononanoic acid (PFNA) was inversely associated with the risk of depression (OR=0.23, 95%CI: 0.14, 0.39), and showed a linear dose-response relationship (Ptrend<0.001, χ2=35.13, Poverall<0.001). After subgroup analysis, it was found that males, 20-39 year-olds and 40-64 year-olds were more sensitive to PFNA exposure (OR=0.15, 95%CI: 0.06, 0.37; OR=0.16, 95%CI: 0.06, 0.40; OR=0.18, 95%CI: 0.08, 0.39). PFOS only showed a statistically significant health effect in people aged 20-39 years (OR=3.00, 95%CI: 1.14, 7.94). In addition, among subgroups of non-Hispanic blacks, cohabitants, current drinkers, high school graduates, and obese patients, exposure to PFAS was significantly associated with the risk of depression. Conclusion PFOS exposure may be associated with increased levels of depression, whereas PFNA exposure may be protective.

A Hybrid Quantum Image-Matching Algorithm.

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.

[Value of procalcitonin in the diagnosis of perioperative infection in patients undergoing primary hip replacement].

OBJECTIVE: To analyze the value of procalcitonin (PCT) in the diagnosis of perioperative infection associated with implants in patients with primary hip arthroplasty. METHODS: A retrospective study was conducted on 150 patients who underwent primary hip arthroplasty from June 2018 to June 2020, including 86 males and 64 females, aged from 47 to 77 years old with an average of (57.04±7.43) years. All patients with primary hip arthroplasty were divided into infection group and non infection group according to whether there was infection after operation. Blood samples were collected from the elbow vein before operation (D0) and on the 4, 6, 8 days after operation(D4, D6 and D8) respectively to detect the serum PCT level and white blood cell count (WBC) level. RESULTS: Among 150 patients with primary hip arthroplasty, 34 patients with postoperative infection were in the infection group, and 116 patients without postoperative infection were in the noninfection group. In the infection group, there were 19 cases of superficial surgical site infection(55.88%, 19/34), 9 cases of urinary tract infection (26.47%, 9/34), and 6 cases of pneumonia(17.65%, 6/34). After bacterial culture in the infection group, there were 9 cases of Staphylococcus aureus, 3 cases of Escherichia coli, 3 cases of Staphylococcus epidermidis, 3 cases of Streptococcus constellation, 3 cases of Candida albicans, 6 cases of Klebsiella pneumoniae, 2 cases of Escherichia coli and Streptococcus agalactis, 3 cases of coagulase invisible staphylococcus and Burkholderia cepacia, 2 cases of Escherichia coli, Enterococcus faecalis and Pseudomonas aeruginosa. There was no significant difference in PCT levels between two groups in D0(P=0.081), D4(P=0.069) and D6(P=0.093), but there was significant difference in D8(P=0.007). There was no significant difference in WBC between two groups at any time point(P>0.05). The results of receiver operating characteristic curve(ROC) showed that the AUC of PCT diagnosis was 0.978[95%CI(0.933, 1.022)] and that of WBC was 0.562[95%CI(0.398, 0.726)], PCT was an important predictor of infection after primary hip arthroplasty(AUC>0.9). When the critical value was 0.526 ng/ml, the sensitivity and specificity of PCT diagnosis are 36% and 100%, respectively, WBC was not a significant predictor of infection after primary hip arthroplasty (0.5

Suppressing the Dielectric Loss in Superconducting Qubits through Useful Geometry Design.

Dielectric loss from different interfacial layers in the superconducting circuit and from external environment may cause superconducting qubit decoherence. Compared to modeling the entire device at once with a numerical solver, quantitatively formulating the dielectric loss can both describe all loss mechanisms and make the optimization more transparent. In this paper, we first analyze the expression formula of dielectric loss, and obtain a design scheme that can reduce the dielectric loss of qubits. That is, we replace the straight junction wires with the tapered junction wires. Based on this scheme, we perform a simulation to optimize the design of junction wires. Finally, a real experiment is conducted to verify our design. The results show that both the T1 time and T2 time of qubits are significantly improved.

O-terminated interface for thickness-insensitive transport properties of aluminum oxide Josephson junctions.

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.

Comparison of Lumped Oscillator Model and Energy Participation Ratio Methods in Designing Two-Dimensional Superconducting Quantum Chips.

Over the past two decades, superconducting quantum circuits have become one of the essential platforms for realizing quantum computers. The Hamiltonian of a superconducting quantum circuit system is the key to describing the dynamic evolution of the system. For this reason, various methods for analyzing the Hamiltonian of a superconducting quantum circuit system have been proposed, among which the LOM (Lumped Oscillator Model) and the EPR (Energy Participation Ratio) methods are the most popular ones. To analyze and improve the design methods of superconducting quantum chips, this paper compares the similarities and differences of the LOM and the EPR quantification methods. We verify the applicability of these two theoretical approaches to the design of 2D transmon quantum chips. By comparing the theoretically simulated results and the experimentally measured data at extremely low temperature, the errors between the theoretical calculation and observed measurement values of the two methods were summarized. Results show that the LOM method has more parameter outputs in data diversity and the qubit frequency calculation in LOM is more accurate. The reason is that in LOM more coupling between different systems are taken into consideration. These analyses would have reference significance for the design of superconducting quantum chips.

A high-performance compilation strategy for multiplexing quantum control architecture.

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.

A new method of software vulnerability detection based on a quantum neural network.

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%.