Performance of Grover search algorithm with diagonalizable noise

It is generally believed that Grover search algorithm (GSA) with quantum noise may quickly lose its quadratic speedup over its classical case. In this paper, we partly agree with that by our new findings as follows. First, we investigate different typical diagonalizable noises represented by Bloch vectors, and the results demonstrate that the success probability decreases exponentially to 1/2 and oscillates around 1/2 with the increase of the number of iterations. Second, for some types of noises, such as bit flip and bit-phase flip noises, can improve the performance of GSA for certain parts of the search process. Third, we calculate and analyze the noise threshold of the bit-phase flip noise for the requested success probability and the result shows that GSA with noise within the threshold still outperforms its classical counterpart. According to the above results, some interesting works in the noisy intermediate-scale quantum (NISQ) computing are suggested, such as verifying the correctness of quantum algorithms even with noises and machine learning applications

A comparative analysis of delay propagation on departure and arrival flights for a chinese case study

In recent years, flight delay costs the air transportation industry millions of dollars and has become a systematic problem. Understanding the behavior of flight delay is thus critical. This paper focuses on how flight delay is affected by operation-, time-, and weather-related factors. Different econometric models are developed to analyze departure and arrival delay. The results show that compared to departure delay, arrival delay is more likely to be affected by previous delays and the buffer effect. Block buffer presents a reduction effect seven times greater than turnaround buffer in terms of flight delays. Departure flights suffer more delays from convective weather than arrival flights. Convective weather at the destination airport for flight delay has a greater impact than at the original airport. In addition, sensitivity analysis of flight delays from an aircraft utilization perspective is conducted. We find that the effect of delay propagation on flight delay differs by aircraft utilization. This impact on departure delay is greater than the impact on arrival delay. In general, specific to the order of flights, the previous delay increases the impact on flight on-time performance as a flight flies a later leg. Buffer time has opposite effects on departure and arrival delay, with the order increasing. A decrease in buffer time with the order increasing, however, still has a greater reduction effect on departure delay than arrival delay. Specific to the number of flights operated by an aircraft, the more flights an aircraft flies in a day, the more the on-time performance of those flights will suffer from the previous delay and buffer time generally

How late does your flight depart? A quantile regression approach for a chinese case study

Flight departure delays cost airlines and airports millions of dollars and become a systematic problem. The on-time performance at an airport is connected to and easily affected by delay propagation from previous operations of flights using the airport. In this paper, we employ both Ordinary Least Square (OLS) and quantile regressions to investigate the impact of various influencing factors on flight departure delay. By using historical flight records and weather information, the impacts of delay propagation-related and other factors are quantified to study the correlations between the explanatory and response variables. Three variables, including previous arrival delay, turnaround buffer time, and the first order of a day, are used to examine the propagation effects. We find that aircraft type, flying on a weekday, and being the first flight of a day have significant impacts on short departure delays. Ground buffer is conducive to mitigating delay propagation. For long delays, however, ground buffer cannot work in an efficient way, and the previous arrival effect is more important. Convective weather and aircraft type are the crucial factors in this situation. Interestingly, flying on a weekday suddenly becomes one of the main components under extreme delays. Meanwhile, propagated delay and airport congestion remain significantly impactful on the on-time performance

Detection and evaluation of abnormal user behavior based on quantum generation adversarial network

Quantum computing holds tremendous potential for processing high-dimensional data, capitalizing on the unique capabilities of superposition and parallelism within quantum states. As we navigate the noisy intermediate-scale quantum (NISQ) era, the exploration of quantum computing applications has emerged as a compelling frontier. One area of particular interest within the realm of cyberspace security is Behavior Detection and Evaluation (BDE). Notably, the detection and evaluation of internal abnormal behaviors pose significant challenges, given their infrequent occurrence or even their concealed nature amidst vast volumes of normal data. In this paper, we introduce a novel quantum behavior detection and evaluation algorithm (QBDE) tailored for internal user analysis. The QBDE algorithm comprises a Quantum Generative Adversarial Network (QGAN) in conjunction with a classical neural network for detection and evaluation tasks. The QGAN is built upon a hybrid architecture, encompassing a Quantum Generator ($G_Q$) and a Classical Discriminator ($D_C$). $G_Q$, designed as a parameterized quantum circuit (PQC), collaborates with $D_C$, a classical neural network, to collectively enhance the analysis process. To address the challenge of imbalanced positive and negative samples, $G_Q$ is employed to generate negative samples. Both $G_Q$ and $D_C$ are optimized through gradient descent techniques. Through extensive simulation tests and quantitative analyses, we substantiate the effectiveness of the QBDE algorithm in detecting and evaluating internal user abnormal behaviors. Our work not only introduces a novel approach to abnormal behavior detection and evaluation but also pioneers a new application scenario for quantum algorithms. This paradigm shift underscores the promising prospects of quantum computing in tackling complex cybersecurity challenges

Gabexate in the prophylaxis of post-ERCP pancreatitis: a meta-analysis of randomized controlled trials

BACKGROUND: Acute pancreatitis is a common complication of endoscopic retrograde cholangiopancreatography and the benefit of its pharmacological treatment is unclear. Although prophylactic use of gabexate for the reduction of pancreatic injury after ERCP has been evaluated, the discrepancy about gabexate's beneficial effect on pancreatic injury still exists. This study aimed to evaluate the effectiveness and safety of gabexate in the prophylaxis of post-endoscopic retrograde cholangiopancreatography pancreatitis (PEP). METHODS: We employed the method recommended by the Cochrane Collaboration to perform a meta-analysis of randomized controlled trials (RCTs) of gabexate in the prevention of post-ERCP pancreatitis (PEP) including three RCTs conducted in Italy and one in China. RESULTS: All of the four RCTs were of high quality. When the RCTs were analyzed, odds ratios (OR) for gabexate mesilate were 0.67 [95% CI (0.31~1.47), p = 0.32] for PEP, 3.78 [95% CI (0.62~22.98), p = 0.15] for severe PEP, 0.68 [95% CI (0.19~2.43), p = 0.56] for the case-fatality of PEP, 0.88 [95% CI (0.72~1.07), p = 0.20] for post-ERCP hyperamylasemia, 0.69 [95% CI (0.39~1.21), p = 0.19] for post-ERCP abdominal pain, thus indicating no beneficial effects of gabexate on acute pancreatitis, the death rate of PEP, hyperamylasemia and abdominal pain. No evidence of publication bias was found. CONCLUSION: Gabexate mesilate can not prevent the pancreatic injury after ERCP. It is not recommended for the use of gabexate mesilate in the prophylaxis of PEP

Cell–cell contact promotes Ebola virus GP-mediated infection

AbstractEbola virus (EBOV) is a highly pathogenic filovirus that causes hemorrhagic fever in humans and animals. Here we provide evidence that cell–cell contact promotes infection mediated by the glycoprotein (GP) of EBOV. Interestingly, expression of EBOV GP alone, even in the absence of retroviral Gag-Pol, is sufficient to transfer a retroviral vector encoding Tet-off from cell to cell. Cell-to-cell infection mediated by EBOV GP is blocked by inhibitors of actin polymerization, but appears to be less sensitive to KZ52 neutralization. Treatment of co-cultured cells with cathepsin B/L inhibitors, or an entry inhibitor 3.47 that targets the receptor NPC1 for virus binding, also blocks cell-to-cell infection. Cell–cell contact also enhances spread of rVSV bearing GP in monocytes and macrophages, the primary targets of natural EBOV infection. Altogether, our study reveals that cell–cell contact promotes EBOV GP-mediated infection, and provides new insight into understanding EBOV spread and viral pathogenesis